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PUBLISHED CONFERENCE ARTICLES


Dimensioning of Telephone Networks using a Neural Network as Traffic Distribution Approximator
Engelbrecht, AP. Cloete, I. 1995.
Proceedings of the International Workshop on the Applications of Neural Networks to Telecommunications, J Alspector, R Goodman, TX Brown (eds), Stockholm, Sweden, pp 72-79, Lawrence Erlbaum Associates

Download this publication from the Neural Networks group.

Abstract:

A feedforward neural network is used to approximate the distribution of both primary (first-offered) traffic and overflow traffic in a telephone network. We show that a neural network accurately approximates both primary and overflow traffic distributions, and if utilized in an automated dimensioning model, reduces the complexity of that model.

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Determining the Significance of Input Parameters using Sensitivity Analysis
Engelbrecht, AP. Cloete, I. Zurada, J. 1995.
International Workshop on Artificial Neural Networks, Torremolinos, Spain, J Mira, F Sandoval (eds), 930:382-388, From natural Science to Artificial Neural Computing, in the Springer-Verlag series Lecture Notes in Computer Science

Download this publication from the Neural Networks group.

Abstract:

Accompanying the application of rule extraction algorithms to real-world problems is the crucial difficulty to compile a representative data set. Domain experts often find it difficult to identify all input parameters that have an influence on the outcome of the problem. In this paper we discuss the problem of identifying relevant input parameters from a set of potential input parameters. We show that sensitivity analysis applied to a trained feedforward neural network is an efficient tool for the identification of input parameters that have a significant influence on any one of the possible outcomes. We compare the results of a neural network sensitivity analysis tool with the results obtained from a machine learning algorithm, and discuss the benefits of sensitivity analysis to a neural network rule extraction algorithm.

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Automatic Scaling using Gamma Learning in Feedforward Neural Networks
Engelbrecht, AP. Cloete, I. Geldenhuys, J. Zurada, J. 1995.
International Workshop on Artificial Neural Networks, Torremolinos, Spain, in J Mira, F Sandoval (eds), 930:374-381, From Natural Science to Artificial Neural Computing, in the Springer-Verlag series Lecture Notes in Computer Science

Download this publication from the Neural Networks group.

Abstract:

Standard error back-propagation requires output data that is scaled to lie within the active area of the activation function. We show that normalizing data to conform to this requirement is not only a time-consuming process, but can also introduce inaccuracies in modelling of the data. In this paper we propose the gamma learning rule for feedforward neural networks which eliminates the need to scale output data before training. We show that the utilization of ``self-scaling'' units results in faster convergence and more accurate results compared to the rescaled results of standard back-propagation.

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A Routing Rule Simulator for Telephone Networks
Hattingh, M. Engelbrecht, AP. Cloete, I. 1995.
ITC St. Petersburg International Teletraffic Seminar, St. Petersburg, Russia, pp 261-269

Download this publication from the Other group.

Abstract:

An investigation into the performance of routing rules is facilitated by a generalized routing rule simulator with the ability to simulate a general class of routing rules for any network topology. In this paper we describe the design of a routing rule simulator for telephone networks. The simulator receives as input a routing rule description -- for which a generalized routing rule description language was designed -- and a network topology description. The routing rule description language allows the simulation of a wide range of routing rules. The output of the simulator includes all traffic parameters which are necessary to establish the performance of the routing rule.

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A Model for the Estimation of Offered Traffic from Measured Traffic Parameters
Engelbrecht, AP. 1995.
ITC St.Petersburg International Teletraffic Seminar, St. Petersburg, Russia

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Abstract:

When planning the future expansion of telephone networks, or when the performance of a telephone network is studied, it is essential that the traffic quantity offered to each origin-destination pair must be available. Since the traffic offered to each origin-destination pair cannot be measured directly by switches, a model is needed to estimate the offered traffic from measurable traffic parameters. This paper describes an iterative traffic interpolation model which estimates the traffic offered to each origin-destination pair from measured link traffic parameters, namely carried traffic and overflow traffic. Results from this model are presented.

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Reduction of Symbolic Rules from Neural Networks using Sensitivity Analysis
Viktor, H. Engelbrecht, AP. Cloete, I. 1995.
IEEE International Joint Conference on Neural Networks, Perth, Australia, pp 1022-1026

Download this publication from the Data Mining group.

Abstract:

This paper shows how sensitivity analysis identifies and eliminates redundant conditions from the rules extracted from trained neural networks, by eliminating irrelevant inputs. This leads to a reduction in the number and size of the rules. The reduced rule set accurately and minimally reflect the classification problems presented. Also, the elimination of redundant input units significantly reduces the combinatorics of the rule extraction algorithm. The resultant rule set compares favorably with traditional symbolic machine learning algorithms.

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A Sensitivity Analysis Algorithm for Pruning Feedforward Neural Networks
Engelbrecht, AP. Cloete, I. 1996.
IEEE International Joint Conference on Neural Networks, Washington DC, USA, 2:1274-1277

Download this publication from the Neural Networks group.

Abstract:

A pruning algorithm, based on sensitivity analysis, is presented in this paper. We show that the sensitivity analysis technique efficiently prunes both input and hidden layers. Results of the application of the pruning algorithm to various N-bit parity problems agree with well-known published results.

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GARTNet: A Genetic Algorithm for Pruning Feedforward Neural Networks
Sevenster, AS. Engelbrecht, AP. 1996.
Proceedings of IMACS Multiconference on Computational Engineering in Systems Applications, Symposium on Control, Optimization and Supervision, P Borne, M Staroswiecki, JP Cassar, S El Khattabi (eds), Lille, France, 2:1106-1111

Download this publication from the Other group.

Abstract:

We present a routing optimization model for telephone networks based on a genetic algorithm. Optimization criteria applicable to the South African telephone network structure are defined. From extensive simulations we select from the basic genetic algorithm operators those best suited for the routing problem. Promising results have been obtained on small and large networks. In these simulations GARTNet has proven itself to be a good candidate solution to the routing optimization problem.

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Selective Learning using Sensitivity Analysis
Engelbrecht, AP. Cloete, I. 1998.
Proceedings of International Joint Conference on Neural Networks, Anchorage, Alaska, pp 1150-1156, IEEE World Congress on Evolutionary Computation

Download this publication from the Neural Networks group.

Abstract:

Research on improving generalization performance and training time of multilayer feedforward neural networks has concentrated mostly on the optimal setting of initial weights, learning rates and momentum, optimal architectures, and sophisticated optimization techniques. In this paper we present an alternative approach where the network dynamically selects patterns during training. We apply sensitivity analysis to select only patterns closest to the separating hyperplanes. Experimental results of an artificial and two real world classification problems show that our selective learning method significantly reduces the training set size without decreasing generalization performance - in fact, the results presented show that generalization is improved compared to learning with all training patterns.

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Optimizing the Number of Hidden Nodes of a Feedforward Artificial Neural Network
Fletcher, L. Katkovnik, V. Steffens, FE. Engelbrecht, AP. 1998.
Proceedings of the International Joint Conference on Neural Networks, pp 1608 - 1612, IEEE World Congress on Computational Intelligence

Unavailable for download.

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Incorporating Rule Extraction from ANNs into a Cooperative Learning Environment
Viktor, H. Engelbrecht, AP. Cloete, I. 1998.
Neural Networks and Their Applications, Marseilles, France, pp 385-391

Download this publication from the Data Mining group.

Abstract:

Rule extraction from artificial neural networks (ANNs) addresses the need of domain experts to obtain insight into the decision making process of an ANN. This paper presents the ANNSER approach to extracting rules from continuous data, using sensitivity analysis to locate decision boundaries in determining the thresholds of attributes. In addition, the paper discusses the incorporation of the ANNSER approach into a rule-based cooperative learning environment. In the cooperative learning environment, the ANNSER approach co-exists with other inductive learning techniques. Therefore, the domain expert may use more than one approach to verify the classification of the concepts that are contained in the data.

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Feature Extraction from Feedforward Neural Networks using Sensitivity Analysis
Engelbrecht, AP. Cloete, I. 1998.
International Conference on Advances in Systems, Signals, Control and Computers, V Bajic (ed), Durban South Africa, 2:221-225

Download this publication from the Neural Networks group.

Abstract:

Sensitivity analysis is a powerful tool to extract meaningful information from trained multilayer feedforward neural networks. A neural network (NN) numerically encodes its knowledge about a problem in the weights of the network. This knowledge is used to generalize to data not seen during training, and can be used to optimize the network architecture, to optimize use of the training set, to enhance rule extraction, and to analyze the function of each hidden unit. This paper shows how sensitivity analysis with respect to the NN output function can be used to achieve these objectives.

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Rule Improvement through Decision Boundary Detection using Sensitivity Analysis
Engelbrecht, AP. Viktor, HL. 1999.
International Working Conference on Artificial Neural Networks, Alicabte, Spain, 1607:78-84, in the Springer-Verlag series Lecture Notes in Computer Science

Download this publication from the Data Mining group.

Abstract:

Rule extraction from artificial neural networks (ANN) provides a mechanism to interpret the knowledge embedded in the numerical weights. Classification problems with continuous-valued parameters create difficulties in determining boundary conditions for these parameters. This paper presents an approach to locate such boundaries using sensitivity analysis. Inclusion of this decision boundary detection approach in a rule extraction algorithm resulted in significant improvements in rule accuracies.

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Variance Analysis of Sensitivity Information for Pruning Multilayer Feedforward Neural Networks
Engelbrecht, AP. Fletcher, L. Cloete, I. 1999.
IEEE International Joint Conference on Neural Networks, Washington DC, USA, paper 379, IEEE

Download this publication from the Neural Networks group.

Abstract:

This paper presents an algorithm to prune feedforward neural network architectures using sensitivity analysis. Sensitivity Analysis is used to quantify the relevance of input and hidden units. A new statistical pruning heuristic is proposed, based on variance analysis, to decide which units to prune. Results are presented to show that the pruning algorithm correctly prunes irrelevant input and hidden units.

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Approximation of a Function and its Derivative in Feedforward Neural Networks
Basson, E. Engelbrecht, AP. 1999.
IEEE International Joint Conference on Neural Networks, Washington DC, USA, paper 2152, IEEE

Download this publication from the Neural Networks group.

Abstract:

A new learning algorithm is presented that learns a function and its first-order derivatives. Derivatives are learned together with the function using gradient descent. Preliminary results show that the algorithm produces acceptable approximations to the derivatives.

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A Hybrid Exhaustive and Heuristic Rule Extraction Approach
Rodich, D. Engelbrecht, AP. 1999.
In: Development and Practice of Artificial Intelligence Techniques, VB Bajic, D Sha (eds), pp 25-28, Proceedings of the International Conference on Artificial Intelligence, Durban, South Africa

Download this publication from the Data Mining group.

Abstract:

This paper presents a new exhaustive-heuristic hybrid approach to the discovery of rules from data sets containing binary attributes. Principles from evolutionary computing are used to design heuristics to reduce the complexity of the search for crisp and accurate rules. A comparison of this new approach with a benchmark genetic algorithm approach shows the proposed method to be more efficient.

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A New Selective Learning Algorithm for Time Series Approximation using Feedforward Neural Networks
Engelbrecht, AP. Adejumo, A. 1999.
In: Development and Practice of Artificial Intelligence Techniques, VB Bajic, D Sha (eds), pp 29-31, Proceedings of the International Conference on Artificial Intelligence, Durban, South Africa

Download this publication from the Neural Networks group.

Abstract:

Various research results have shown that training on a fixed set of patterns do not produce best results. Much gain can be achieved by dynamically changing the contents of the training set, during training, to reflect patterns which are most informative to the training objective. This paper presents a training strategy which orders time series training data after each epoch into large-next-day changes and small-next-day changes training subsets. The training strategy then selects patterns more frequently from the large-next-day changes subset.

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A Comparative Study of Neural Network Active Learning Algorithms
Adejumo, A. Engelbrecht, AP. 1999.
In: Development and Practice of Artificial Intelligence Techniques, VB Bajic, D Sha (eds), pp 31-35, Proceedings of the International Conference on Artificial Intelligence, Durban, South Africa

Download this publication from the Neural Networks group.

Abstract:

Active learning has emerged as an efficient alternative to improve the performance of multilayer feedforward neural networks. The learner is given active control over which information to include in the training set, and in doing so, the generalization accuracy is improved compared to training on a fixed set of data. This paper presents a comparison of such active learning algorithms.

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Training Product Units in Feedforward Neural Networks using Particle Swarm Optimization
Ismail, A. Engelbrecht, AP. 1999.
In: Development and Practice of Artificial Intelligence Techniques, VB Bajic, D Sha (eds), pp 36-40, Proceedings of the International Conference on Artificial Intelligence, Durban, South Africa

Download this publication from the Neural Networks and Swarm Intelligence groups.

Abstract:

Product unit (PU) neural networks are powerful because of their ability to handle higher order combinations of inputs. Training of PUs by backpropagation is however difficult, because of the introduction of more local minima. This paper compares training of a product unit neural network using particle swarm optimization with training of a PU using gradient descent.

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Searching the Forest: Using Decision Trees as Building Blocks for Evolutionary Search in Classification Databases
Rouwhorst, S. Engelbrecht, AP. 2000.
IEEE International Congress on Evolutionary Computation, San Diego, USA, pp 633-638, IEEE

Download this publication from the Evolutionary Computing and Data Mining groups.

Abstract:

A new evolutionary search algorithm, called BGP, to be used for classification tasks in data mining, is introduced. It is different from existing evolutionary techniques in that it does not use indirect representations of a solution, such as bit strings or grammars. The algorithm uses decision trees of various sizes as individuals in the populations and operators, e.g. crossover, are performed directly on the trees. When compared to C4.5 and CN2 on a benchmark of problems, BGP shows very good results.

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Global Optimization Algorithms for Training Product Unit Neural Networks
Ismail, A. Engelbrecht, AP. 2000.
IEEE International Conference on Neural Networks, Como, Italy, paper 032, IEEE

Download this publication from the Neural Networks and Swarm Intelligence groups.

Abstract:

Product units in the hidden layer of multilayer neural networks provide a powerful mechanism for neural networks to efficiently learn higher-order combinations of inputs. Training product unit networks using local optimization algorithms is difficult due to an increased number of local minima and increased chances of network paralysis. This paper discusses the problems with using gradient descent to train product unit neural networks, and shows that particle swarm optimization, genetic algorithms and LeapFrog are efficient alternatives to successfully train product unit neural networks.

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Data Generation using Sensitivity Analysis
Engelbrecht, AP. 2000.
International Symposium on Computational Intelligence, Kosice, Slovakia

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Abstract:

This paper presents a new approach to the generation of training data for supervised neural networks. Sensitivity analysis is used to find the most informative regions in input space. Knowledge of these informative regions can be used to remove redundant training patterns and to generate additional training patterns withing the informative regions. Preliminary experimental results show that this apporach to data generation holds much promise.

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Effects of Swarm Size on Cooperative Particle Swarm Optimizers
van den Bergh, F. Engelbrecht, AP. 2001.
Genetic and Evolutionary Computation Conference, San Francisco, USA

Download this publication from the Swarm Intelligence group.

Abstract:

Particle Swarm Optimisation is a stochastic global optimisation technique making use of a population of particles, where each particle represents a solution to the problem being optimised. The Cooperative Particle Swarm Optimiser (CPSO) is a variant of the original Particle Swarm Optimiser (PSO). This technique splits the solution vector into smaller vectors, where each sub-vector is optimised using a separate PSO. This paper investigates the effect the swarm size on the CPSO, showing that a swarm size of only 10 particles is usually sufficient.

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Selective Learning for Multilayer Feeforward Neural Networks
Engelbrecht, AP. 2001.
International Work-Conference on Artificial Neural Networks, Granada, Spain, In: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence, J Mira, A Prieto (eds), Part I, pp 386-393, Springer-Verlag series Lecture Notes in Computer Science, Vol 2084

Download this publication from the Neural Networks group.

Abstract:

Selective learning is an active learning strategy where the neural network selects during training the most informative patterns. This paper investigates a selective learning strategy where the informativeness of a pattern is measured as the sensitivity of the network output to perturbations in that pattern. The sensitivity approach to selective learning is then compared with an error selection approach where pattern informativeness is defined as the approximation error.

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A Cluster Approach to Incremental Learning
Brits, R. Engelbrecht, AP. 2001.
IEEE International Conference on Neural Networks, Washington DC, USA

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Abstract:

The sensitivity analysis approach to incremental learning presented in \cite{eng99} is extended in this paper. The approach in \cite{eng99} selects at each subset selection interval only one new informative pattern from the candidate training set, and adds the selected pattern to the current training subset. This approach is extended with an unsupervised clustering of the candidate training set. The most informative pattern is then selected from each of the clusters. Experimental results are given to show that the clustering approach to incremental learning performs substantially better than the original approach in \cite{eng99}.

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Using Cooperative Particle Swarm Optimization to Train Product Unit Neural Networks
van den Bergh, F. Engelbrecht, AP. 2001.
IEEE International Joint Conference on Neural Networks, Washington DC, USA

Download this publication from the Swarm Intelligence group.

Abstract:

The Cooperative Particle Swarm Optimiser (CPSO) is a variant of the Particle Swarm Optimiser (PSO) that splits the problem vector, for example a neural network weight vector, across several swarms. This paper investigates the influence that the number of swarms used (also called the split factor) has on the training performance of a Product Unit Neural Network. Results are presented, comparing the training performance of the two algorithms, PSO and CPSO, as applied to the task of training the weight vector of a Product Unit Neural Network.

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Pruning Product Unit Neural Networks
Ismail, A. Engelbrecht, AP. 2002.
Proceedings of International Joint Conference on Neural Networks, Honolulu, Hawaii , IEEE World Congress on Computational Intelligence

Download this publication from the Neural Networks group.

Abstract:

Selection of the optimal architecture of a neural network is crucial to ensure good generalization by reducing the occurrence of overfitting. While much work has been done to develop pruning algorithms for networks that employ summation units, not much has been done on pruning of product unit neural networks. This paper develops and tests a pruning algorithm for product unit networks, and illustrates its performance on several function approximation tasks.

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Solving Systems of Unconstrained Equations using Particle Swarm Optimization
Brits, R. Engelbrecht, AP. van den Bergh, F. 2002.
IEEE Conferece on Systems, Man, and Cybernetics, Tunisa

Download this publication from the Swarm Intelligence group.

Abstract:

A new particle swarm optimization algorithm (PSO), nbest, is developed in this paper to solve systems of unconstrained equations. For this purpose, the standard gbest PSO is adapted by redefining the fitness function in order to locate multiple solutions in one run of the algorithm. The new algorithm also introduces the concept of shrinking particle neighborhoods. The resulting nbest algorithm is a first attempt to develop a niching PSO algorithm. The paper presents results that show the new PSO algorithm to be successful in locating multiple solutions.

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A New Locally Convergent Particle Swarm Optimizer
van den Bergh, F. Engelbrecht, AP. 2002.
IEEE Conference on Systems, Man, and Cybernetics

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Abstract:

This paper introduces a new Particle Swarm Optimisation (PSO) algorithm with strong local convergence properties. The new algorithm performs much better with a smaller number of particles, compared to the original PSO. This property is desirable when designing a niching PSO algorithm.

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Learning to Play Games using a PSO-based Competitive Learning Approach
Messerschmidt, L. Engelbrecht, AP. 2002.
4th Asia-Pacific Conference on Simulated Evolution and Learning

Download this publication from the Swarm Intelligence group.

Abstract:

A new competitive approach is developed for learning agents to play two-agent games. This approach uses particle swarm optimizers (PSO) to train neural networks to predict the desirability of states in the end nodes of a game tree. The new approach is applied to the TicTacToe game, and compared to the performance of the evolutionary approach developed by Fogel. The results show that the new PSO-based approach outperforms the evolutionary approach.

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Image Classification using Particle Swarm Optimization
Omran, M. Salman, A. Engelbrecht, AP. 2002.
4th Asia-Pacific Conference on Simulated Evolution and Learning

Download this publication from the Swarm Intelligence and Image Analysis groups.

Abstract:

A new image classification algorithm that is based on the particle swarm optimizer (PSO) is proposed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar pixels. The new image classifier has been applied successfully to three types of images to illustrate its wide applicability. These images include synthesized, MRI and satellite images. The proposed algorithm is compared with the benchmark classification algorithm, ISODATA, yielding promising results.

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A Niching Particle Swarm Optimizer
Brits, R. Engelbrecht, AP. van den Bergh, F. 2002.
4th Asia-Pacific Conference on Simulated Evolution and Learning

Download this publication from the Swarm Intelligence group.

Abstract:

This paper describes a technique that extends the unimodal particle swarm optimizer to efficiently locate multiple optimal solutions in multimodal problems. Multiple subswarms are grown from an initial particle swarm by monitoring the fitness of individual particles. Experimental results show that the proposed algorithm can successfully locate all maxima on a small set of test functions during all simulation runs.

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Structural Optimization of Learned Polynomial Expressions using Genetic Algorithms
Potgieter, G. Engelbrecht, AP. 2002.
4th Asia-Pacific Conference on Simulated Evolution and Learning

Download this publication from the Evolutionary Computing group.

Abstract:

This paper presents a genetic algorithm approach to construct optimal polynomial expressions to characterize the function described by a set of data points. The algorithm learns structurally optimal polynomial expressions, through the use of specialized shrink and expand mutation operators. The algorithm also optimizes the learning process by using an efficient, fast data clustering algorithm to reduce the training pattern space. Experimental results are compared with results obtained from a neural network. These results show that the genetic algorithm technique is substantially faster than the neural network, and produces comparable accuracy.

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Scalability of Niche PSO
Brits, R. Engelbrecht, AP. van den Bergh, F. 2003.
IEEE Swarm Intelligence Symposium, Indianapolis, pp 228 - 234

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Abstract:

In contrast to optimization techniques intended to find a single, global solution in a problem domain, niching (speciation) techniques have the ability to locate multiple solutions in multimodal domains. Numerous niching techniques have been proposed, broadly classified as temporal (locating solutions sequentially) and parallel (multiple solutions are found concurrently) techniques. Most research efforts to date have considered niching solutions through the eyes of genetic algorithms (GAs), studying simple multimodal problems. Little attention has been given to the possibilities associated with emergent swarm intelligence techniques. Particle swarm optimization (PSO) utilizes properties of swarm behaviour not present in evolutionary algorithms such as GAs, to rapidly solve optimization problems. This paper investigates the ability of two genetic algorithm niching techniques, sequential niching and deterministic crowding, to scale to higher dimensional domains with large numbers of solutions, and compare their performance to a PSO-based niching technique, NichePSO.

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Using Neighborhoods with Guaranteed Convergence PSO
Peer, ES. van den Bergh, F. Engelbrecht, AP. 2003.
IEEE Swarm Intelligence Symposium, Indianapolis, pp 235-242

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Abstract:

The standard Particle Swarm Optimiser (PSO) may prematurely converge on suboptimal solutions that are not even guaranteed to be local extrema. The guarenteed convergence modificationsto the PSO algorithm ensure that the PSO at least converges on a local extremum at the expense of even faster convergence. This faster convergence means that less of the search space is explored reducing the opportunity of the swarm to find better local extrema. Various neighbourhood topologies inhibit premature convergence by preserving swarm diversity during the search. This paper investigates the performance of the Guaranteed Convergence PSO (GCPSO) using different neighbourhood topologies and compares the results with their standard PSO counterparts.

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Training Support Vector Machines with Particle Swarms
Paquet, U. Engelbrecht, AP. 2003.
International Joint Conference on Neural Networks, Portland, OR, 2003

Download this publication from the Swarm Intelligence and Neural Networks groups.

Abstract:

Training a Support Vector Machine requires solving a constrained quadratic programming problem. Linear Particle Swarm Optimization is intuitive and simple to implement, and is presented as an alternative to current numeric SVM training methods. Performance of the new algorithm is demonstrated on the MNIST character recognition dataset.

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Social Networks as a Coordination Technique for Multi-Robot Systems
Rodic, D. Engelbrecht, AP. 2003.
International Conference on System Design and Applications, Oklahoma

Download this publication from the Multi-Agent Systems group.

Abstract:

The last decade saw a renewed interest in the robotics research field and a shift in research focus. In the eighties and early nineties, the focus of robotic research was on finding optimal robot architectures, often resulting in non-cognitive, insect-like entities. In recent years, processing power available to autonomous agents has improved and that has allowed for more complex robot architectures. The focus has shifted from single robot to multi-robot teams. The key to the full utilisation of multi-robot teams lies in cooperation. Although a robot is a special case of an agent, many existing multi-agent cooperation techniques could not be directly ported to multi-robot teams. In this paper, we overview mainstream multi-robot coordination techniques and propose a new approach to coordination, based on models of organisational sociology. The proposed coordination model is not robot specific, but it can be applied to any multi-agent system without any modifications.

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Computer Aided Identification of Biological Specimens using Self-Organizing Maps
Dean, EJ. Engelbrecht, AP. Nicholas, A. 2003.
Fourth International Conference on Data Mining, Rio de Janeiro, 2003

Download this publication from the Neural Networks group.

Abstract:

It is often necessary or desirable that biological material be identified. However, given that there is an estimated 10 million living organisms on Earth, the identification of biological material can be problematic and consequently the services of a taxonomist specialist are often required. If such an expert is not readily available it is necessary to attempt an identification using an alternative method; but some of the alternative methods available are unsatisfactory or can lead to a wrong identification. One of the most common problems encountered when identifying specimens is that important diagnostic features are often not easily observed, or may even be completely absent. A number of techniques can be used to try to overcome this problem, one of which, the Self Organizing Map (or SOM), is a particularly appealing technique because of its ability to handle missing data. This paper explores the use of SOMs as a technique for the identification of indigenous trees of Acacia in KwaZulu-Natal, South Africa. The ability of the SOM technique to perform exploratory data analysis through data clustering is utilized and assessed, as is it?s usefulness for visualizing the results of the analysis of numerical, multivariate botanical datasets. The SOM?s ability to investigate, discover and interpret relationships within these datasets is examined, and the technique?s ability to identify tree species successfully is tested. The tests performed so far have provided promising results and suggest that the application of the SOM to the problem of identification could provide the breakthrough in computerized identification for which botanists have long been hoping.

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INDABA - Proposal for an Intelligent Distributed Agent Based Architecture
Rodic, D. Engelbrecht, AP. 2003.
Second International Conference on Computational Intelligence, Robotics and Autonomous Systems, Singapore, 2003

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Abstract:

This paper presents an overview of Intelligent Distributed Agent Based Architecture (INDABA), a framework for building socially aware Multi Agent Systems (MAS) that is currently under development as a part of the research effort at the University of Pretoria. The guiding idea behind this framework is not to necessarily improve on every single aspect of multi-agent systems (such as agent architecture, interaction language etc.), but to provide a test bed for experimenting with various interaction mechanisms. Effort has been made to keep our architecture as standard as possible, to allow for possible joint efforts between various entities (industry, academic institutions and research centres). Many mechanisms described as a part of INDABA are already in development and for the others the overall guiding ideas for their implementation are presented. The proposed architecture will be a test bed for a multidisciplinary research effort that will include elements of artificial intelligence, sociology, artificial life and biology. However, the main focus is in embedded multi-agent systems, robotic teams. The purpose of this article is, therefore to describe the laying of a foundation for a new, hybrid, architecture. In addition, in this paper we also overview a coordination mechanism loosely based on Contract Net Protocol that, through innovative application of social networks, incorporates some elements of artificial life, i.e. like competition and specialisation.

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Investigation of Low Cost Hybrid Three-Layer Robot Architecture
Rodic, D. Engelbrecht, AP. 2003.
Second International Conference on Computational Intelligence, Robotics and Autonomous Systems, Singapore, 2003

Download this publication from the Multi-Agent Systems group.

Abstract:

The robotics research has become one of the most active research fields in the domain of artificial intelligence. Over the last decade, researchers have proposed numerous robot architecture models, quite often very complex in nature. Most of those architectures were implemented only in simulations. The real test of an architecture is its performance in real world embodied robots, and it would be beneficial if physical robots were used instead of simulations. One of the most popular, albeit fairly complex, architecture models is the so called "three-layer" architecture. Unfortunately, on the one hand real robots complex enough to support such architecture, are often costly and difficult to construct . On the other hand, there is abundance of well-tested, commercially available robots that have limited processing power. In this paper, we propose an implementation of "three-layer" derivative architecture on a hybrid robotic platform that consists of a PC and an off-the-shelf LEGO robot.

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Investigation into the Applicability of Social Networks as a Task Allocation Tool for Multi-Robot Teams
Rodic, D. Engelbrecht, AP. 2003.
Second International Conference on Computational Intelligence, Robotics and Autonomous Systems, Singapore, 2003

Download this publication from the Multi-Agent Systems group.

Abstract:

Robotics research has become one of the most active research fields in the domain of artificial intelligence. Over the last decade, researchers have proposed numerous robot architecture models, quite often very complex in nature. Most of those architectures were implemented only in simulations. The real test of an architecture is its performance in real world embodied robots, and it would be beneficial if physical robots were used instead of simulations. One of the most popular, albeit fairly complex architecture models is the so called ``three-layer'' architecture. Unfortunately, on the one hande real robots complex enought to support such architecture, are often costly and difficult to construct. On the other hand, there is abundance of well-tested, commercially available robots that have limited processing power. In this paper, we propose an implementation of ``three-layer'' derivative architecture on a hybrid robotic platform that consits of a PC and an off-the-shelf LEGO robot.

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CIRG@UP OptiBench: A Statistically Sound Framework for Benchmarking Optimisation Algorithms
Peer, ES. Engelbrecht, AP. van den Bergh, F. 2003.
IEEE Congress on Evolutionary Computation, Canberra, Australia, 2003, 2386-2392, IEEE

Download this publication from the Swarm Intelligence group.

Abstract:

This paper is a proposal, by the Computational Intelligence Research Group at the University of Pretoria (CIRG@UP), for a framework to benchmark optimisation algorithms. This framework, known as OptiBench, was conceived out of the necessity to consolidate the efforts of a large research group. Many problems arise when different people work independently on their own research initiatives. These problems range from duplicating effort to, more seriously, having conflicting results. In addition, less experienced members of the group are sometimes unfamiliar with the necessary statistical methods required to properly analyse their results. These problems are not limited internally to CIRG@UP but are also prevalent in the research community at large. This proposal aims to standardise the research methodology used by CIRG@UP internally (initially in the optimisation subgroup and later in subgroups working in other paradigms of computational research). Obviously this paper cannot dictate the methodologies that should be used by other members of the broader research community, however, the hope is that this framework will be found useful and that others will willingly contribute and become involved.

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Data Clustering using Particle Swarm Optimization
van der Merwe, DW. Engelbrecht, AP. 2003.
IEEE Congress on Evolutionary Computation, Canberra, Australia, 2003, 215-220, IEEE

Download this publication from the Swarm Intelligence group.

Abstract:

This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.

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A New Particle Swarm Optimiser for Linearly Constrained Optimisation
Paquet, U. Engelbrecht, AP. 2003.
IEEE Congress on Evolutionary Computation, Canberra, Australia, 2003, 227-233, IEEE

Download this publication from the Swarm Intelligence and Neural Networks groups.

Abstract:

A new PSO algorithm, the Linear PSO (LPSO), is developed to optimise functions constrained by linear constraints of the form Ax = b. A crucial property of the LPSO is that the possible movement of particles through vector spaces is guaranteed by the velocity and position update equations. This property makes the LPSO ideal in optimising linearly constrained problems. The LPSO is extended to the Converging Linear PSO, which is guaranteed to always find at least a local minimum.

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Comparing PSO Structures to Learn the Game Checkers from Zero Knowledge
Franken, N. Engelbrecht, AP. 2003.
IEEE Congress on Evolutionary Computation, Canberra, Australia, 2003, 234-241, IEEE

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Abstract:

This paper investigates the effectiveness of various particle swarm optimiser structures to learn how to play the game of checkers. Co-evolutionary techniques are used to train the game playing agents. Performance is compared against a player making moves at random. Initial experimental results indicate definite advantages in using certain information sharing structures and swarm size configurations to successfully learn the game of checkers.

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PSO approaches to co-evolve IPD strategies
Franken, N. Engelbrecht, AP. 2004.
IEEE Congress on Evolutionary Computation, Portland Oregon, USA, 2004 , IEEE

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Abstract:

This paper investigates two different approaches using Particle Swarm Optimisation (PSO) to evolve strategies for the Iterated Prisoner's Dilemma (IPD). Strategies evolved by the lesser known Binary PSO algorithm are compared to strategies evolved by neural networks that were trained using PSO. Evolved strategies are compared against well-known game theory strategies, with positive results. The presence of noise during IPD interactions are also investigated, and evolved strategies are compared against the same well-known game theory strategies in a noisy environment.

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Using Vector Operations to Identify Niches for Particle Swarm Optimization
Schoeman, L. Engelbrecht, AP. 2004.
IEEE Conference of Cybernetics and Intelligent Systems, Singapore

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A Parrallel Vector-Based Particle Swarm Optimizer
Scheman, L. Engelbrecht, AP. 2005.
International Conference on Neural Networks and Genetic Algorithms, Portugal:268-271

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Abstract:

Not Available

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Coevolving Probabilistic Game Playing Agents using Particle Swarm Optimization Algorithms
Papacostantis, E. Engelbrecht, AP. Franken, N. 2005.
IEEE Symposium on Computational Intelligence and Games, Colchester, Essex, UK, 2005, 195-202, IEEE

Download this publication from the Games group.

Abstract:

Coevolutionary techniques in combination with particle swarm optimization algorithms and neural networks have shown to be very successful in finding strong game playing agents for a number of deterministic games. This paper investigates the applicability of a PSO coevolutionaryapproach to probabilistic games. For the purposes of this paper, a probabilistic variation of the tic-tac-toe game is used. Initially, the technique is applied to a simple deterministic game (tic-tac-toe), proving its effectiveness with such games. The technique is then applied to a probabilistic 4x4x4 tic-tac-toe game, illustrating scalability to more complex, probabilistic games. The performance of the probabilistic game agent is compared against agents that move randomly. To determine how these game agents compete against strong non-random game playing agents, coevolved solutions are also compared against agents that utilize a strong hand-crafted static evaluation function. Particle swarm optimization parameters/topologies and neural network architectures are experimentally optimized for the probabilistic tic-tac-toe game.

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Differential Evolution Methods for Unsupervised Image Classification
AP, MGH. Salman, A. Engelbrecht, AP. 2005.
IEEE Congress on Evolutionary Computation, Edingurgh, Scotland

Unavailable for download.

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Combining Particle Swarm Optimisation with Angle Modulation to Solve Binary Problems
Pamara, G. Franken, N. Engelbrecht, AP. 2005.
IEEE Symposium on Computational Intelligence and Games, Colchester, Essex, UK, 2005, 195-202, IEEE

Unavailable for download.

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Nonlinear Mapping using Particle Swarm Optimisation
Edwards, A. Franken, N. Engelbrecht, AP. 2005.
IEEE Congress of Evolutionary Computation, Edingurgh, Scotland

Unavailable for download.

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Investigating Binary PSO Parameter Influence on the Knights Cover Problem
Franken, N. Engelbrecht, AP. 2005.
IEEE Congress of Evolutionary Computation, Edingurgh, Scotland

Unavailable for download.

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Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classigication
Omran, M. Engelbrecht, AP. Salman, A. 2005.
Fifth World Enformatika Conference, Prague. Vol 9

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Abstract:

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Self-Adaptive Differential Evolution
Omran , M. Engelbrecht, AP. Salman, A. 2005.
International Conference on Computational Intelligence and Security, China. Notes in Computer Science vol 3801:192-199

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Abstract:

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Using Neighborhood Topologies with Differential Evolution
Omran , M. Engelbrecht, AP. Salman, A. 2005.
International Conference on Computational Intelligence and Security, China.

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Abstract:

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CiClops: Computational Intelligence Collaborative Laboratory of Pantological Software
Peer, ES. Engelbrecht, AP. Pampara, G. Masiye, BS. 2005.
Proceedings of the IEEE Swarm Intelligence Symposium

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Abstract:

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Niching Ability of Basic Particle Swarm Optimization Algorithms
Engelbrecht, AP. Masiye, BS. Pampara, G. 2005.
Proceedings of the IEEE Swarm Intelligence Symposium

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Abstract:

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Particle Swarm Optimization: Where does It Belong?
Engelbrecht, AP. 2006.
IEEE Swarm Intelligence Symposium

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Abstract:

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Training Bao Game-Playing Agents using Coevolutionary Particle Swarm Optimization
Conradie, J. Engelbrecht, AP. 2006.
IEEE Symposium on Computational Intelligence in Games

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Abstract:

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Self Adaptive Differential Evolution Methods for Unsupervised Image Classification
Omran OGH. Engelbrecht, AP. 2006.
IEEE International Conference on Cybernetics and Intelligence Systems

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Abstract:

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Binary Differential Evolution
Pampara, G. Engelbrecht, AP. Franken, N. 2006.
IEEE World Congress on Computational Intelligence

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Abstract:

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Comparing Optimisation Algorithms for Nonlinear Mapping
Edwards, AI. Engelbrecht, AP. 2006.
IEEE World Congress on Computational Intelligence

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Abstract:

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Determining RNA Secondary Structure using Set-based Particle Swarm Optimization
Neethling M. Engelbrecht, AP. 2006.
IEEE World Congress on Computational Intelligence

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Abstract:

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Using the Ring Neighborhood Topology with Self-Adaptive Differential Evolution
Omran M. Engelbrecht, AP. Salman, A. 2006.
International Conference on Nature in Computation

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Abstract:

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Niching for Dynamic Environments using Particle Swarm Optimization
Schoeman, L. Engelbrecht, AP. 2006.
International Conference on Simulated Evolution and Learning

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Abstract:

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Fully Informed Differential Evolution
Omran, MGH. Engelbrecht, AP. Salman, A. 2006.
International Conference on Computational Intelligence and Security

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Abstract:

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A Starting-Time Based Approach to Production with Particle Swarm Optimization
Grobler, J. Engelbrecht, AP. Joubert, JW. Kok, S 2007.
Proceedings of the IEEE Symposium on Computational Intelligence in Scheduling

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Abstract:

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Tournament Particle Swarm Optimization
Duminy, W. Engelbrecht, AP. 2007.
Proceedings of the IEEE Symposium on Computational Intelligence and Games

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Abstract:

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Differential Evolution Based Particle Swarm Optimization
Omran, MGH. Engelbrecht, AP. Salman, A. 2007.
Proceedings of the IEEE Swarm Intelligence Symposium

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Abstract:

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Barebones Particle Swarm for Integer Programming Problems
Omran, MGH. Engelbrecht, AP. Salman, A. Alsharhan, S. 2007.
Proceedings of the IEEE Swarm Intelligence Symposium

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Abstract:

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A scheduling-specific modeling approach for real world scheduling
Grobler, J. Engelbrecht, AP. 2007.
Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management

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Abstract:

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Differential Evolution in High-Dimensional Search Space
Olorunda, O. Engelbrecht, AP. 2007.
IEEE Congress on Evolutionary Computation

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Abstract:

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Local Network Neighborhood Artificial Immune System for Data Clustering
Graaff, AJ. Engelbrecht, AP. 2007.
IEEE Congress on Evolutionary Computation

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Abstract:

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Differential Evolution for Integer Programming Problems
Omran, MGH. Engelbrecht, AP. 2007.
IEEE Congress on Evolutionary Computation

Not Available For Download.

Abstract:

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Binary Differential Evolution Strategies
Engelbrecht, AP. Pampara, G. 2007.
IEEE Congress on Evolutionary Computation

Not Available For Download.

Abstract:

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Enhancing the NichePSO
Engelbrecht, AP. van Loggerenberg, LNH. 2007.
IEEE Congress on Evolutionary Computation

Not Available For Download.

Abstract:

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Self-Adaptive Barebones Differential Evolution
Omran, MGH. Engelbrecht, AP. Salman, A. 2007.
IEEE Congress on Evolutionary Computation

Not Available For Download.

Abstract:

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Cllib: A Collaborative Framework for Computational Intelligence - Part 1
Pampara, G. Engelbrecht, AP. Cloete, T. 2008.
IEEE International Joint Conference on Neural Networks

Not Available For Download.

Abstract:

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Cllib: A Collaborative Framework for Computational Intelligence - Part 2
Cloete, T. Engelbrecht, AP. Pampara, G. 2008.
IEEE International Joint Conference on Neural Networks

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Abstract:

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Cooperative Charged Particle Swarm Optimiser
Rakitianskaia, A. Engelbrecht, AP. 2008.
IEEE Congress on Evolutionary Computation

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Abstract:

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Measuring Exploration/Exploitation in Particle Swarms using Swarm Diversity
Olorunda, O. Engelbrecht, AP. 2008.
IEEE Congress on Evolutionary Computation

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Abstract:

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Solving Dynamic Multi-Objective Problems with Vector Evaluated Particle Swarm Optimisation
Greeff, M. Engelbrecht, AP. 2008.
IEEE Congress on Evolutionary Computation

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Abstract:

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Improved Differential Evolution for Dynamic Optimization Problems
du Plessis, MC. Engelbrecht, AP. 2008.
IEEE Congress on Evolutionary Computation

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Abstract:

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Towards a Self Regulating Local Network Neighborhood Artificial Immune System for Data Clustering
Graaff, AJ. Engelbrecht, AP. 2008.
IEEE Congress on Evolutionary Computation

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Abstract:

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Algorithm Comparisons and the Significance op Population Size
Malan, KM. Engelbrecht, AP. 2008.
IEEE Congress on Evolutionary Computation

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Abstract:

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Multi-Objective DE and PSO Strategies for Production Scheduling
Grobler, J. Engelbrecht, AP. Yadavalli, VSS 2008.
IEEE Congress on Evolutionary Computation

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Abstract:

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A Comparison of Map Neuron Labeling Approaches for Unsupervised Self-Organizing Feature Maps
van Heerden, WS. Engelbrecht, AP. 2008.
IEEE International Joint Conference on Neural Networks

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Abstract:

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Multi-Objective Particle Swarm Optimization for Complex Job Shop Scheduling
Grobler, J. Engelbrecht, AP. Yadavalli, S. Kok, S. 2008.
International Federation of Operational Research Societies Conference

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Abstract:

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A Fuzzy Ant Colony Optimization Algorithms for Topology Design of Distributed Local Area Networks
Khan, SH. Engelbrecht, AP. 2008.
IEEE SIS

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Abstract:

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Particle Swarm Optimization with Spatially Meaningful Neighbours
Lane, J. Engelbrecht, AP. Gain, J. 2008.
IEEE SIS

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Abstract:

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Evaluation of Fitness Functions for Evolved Stock Market Forecasting
Nicholls, JF. Engelbrecht, AP. Malan, K. 2008.
IEEE CIEF

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Abstract:

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Cllib: A Component-based Framework for Plau-and-Simulate Hybrid Computational Intelligence Systems
Engelbrecht, AP. 2008.
International Conference on Hybrid Intelligent Systems

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Abstract:

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HybridSOM: A Generic Rule Extraction Framework for Self-Organizing Feature Maps
van Heerden, W. Engelbrecht, AP. 2009.
International Conference on Datamining

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Abstract:

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Training Neural Networks with PSO in Dynamic Environments
Rakitianskaia, A. Engelbrecht, AP. 2009.
IEEE Congress on Evolutionary Computation

Not Available For Download.

Abstract:

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Adaptive Genetic Programming for Dynamic Classification Problems
Riekert, M. Malan, KM. Engelbrecht, AP. 2009.
IEEE Congress on Evolutionary Computation

Not Available For Download.

Abstract:

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Scalability of the Vector-based Particle Swarm Optimizer
Schoeman, IL. Engelbrecht, AP. 2009.
IEEE Congress on Evolutionary Computation

Not Available For Download.

Abstract:

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An Analysis of Heterogeneous Cooperative Algorithms
Olorunda, O. Engelbrecht, AP. 2009.
IEEE Congress on Evolutionary Computation

Not Available For Download.

Abstract:

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Free Search Differential Evolution
Omran, MGH. Engelbrecht, AP. 2009.
IEEE Congress on Evolutionary Computation

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Abstract:

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Employing the Flocking Behavior of Birds for Controlling Congestion in Autonomous Decentralized Networks
Antoniou, P. Pitsillides, A. Blackwell, T. Engelbrecht, AP. 2009.
IEEE Congress on Evolutionary Computation

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Abstract:

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Quantifying Ruggedness of Continuous Landscapes using Entropy
Malan, KM. Engelbrecht, AP. 2009.
IEEE Congress on Evolutionary Computation

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Abstract:

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Implementation of Binary Particle Swarm Optimization for DNA Sequence Design
Khalid, NK. Ibrahim, Z. Kurniawan, TB. Khalid, M. Engelbrecht, AP. 2009.
International Symposium on Distributed Computing and Artificial Intelligence

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Abstract:

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Application of Ordered Weighted Averaging and Unified And-Or-Operators for Multi-Objective Particle Swarm Optimization Algorithms
Khan, SA. Engelbrecht, AP. 2009.
International Conference on Fuzzy Systems and Knowledge Discovery

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Abstract:

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Niche Particle Swarm Optimization for Neural Network Ensembles
Castiello, C. Nitschke, G. Engelbrecht, AP. 2009.
European Conference on Artificial Life

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Abstract:

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Finding Multiple Solutions to Unconstrained Optimixation Problems Using Particle Swarm Optimization
Engelbrecht, AP. 2009.
International Conference on Mathematical and Computational Models

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Abstract:

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Overfitting by PSO Trained Feedforward Neural Networks
van Wyk, AB. Engelbrecht, AP. 2010.
IEEE CEC

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Abstract:

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Alternative Hyper-Heuristic Strategies for Multi-Method Global Optimization
Grobler, J. Engelbrecht, AP. Kendall, G. Yadavalli, VSS. 2010.
IEEE CEC

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Abstract:

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Swarm Tetris: Applying Particle Swarm Optimization to Tetris
Langehoven, L. van Heerden, W. Engelbrecht, AP. 2010.
IEEE CEC

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Effect of Particle Initialization on the Performance of Particle Swarm Niching Algorithms
Schoeman, IL. Engelbrecht, AP. 2010.
Seventh International Conference on Swarm Intelligence

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Abstract:

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Heterogeneous Particle Swarm Optimization
Engelbrecht, AP. 2010.
Seventh International Conference on Swarm Intelligence

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Abstract:

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Techniques for Characterising Fitness Lanscape Complexity: How They have Evolved and a Way Forward
Malan, KM. Engelbrecht, AP. 2010.
accepted for META

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Abstract:

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Mimicking the Bird Flocking Behavior for Controlling Congestion in Sensor Networks
Antoniou, P. Pitsillides, A. Engelbrecht, AP. Blackwell, T. 2010.
Proceedings of the Third International Symposium on Applied Sciences in Biomedical And Communication Technologies

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Abstract:

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Scalability of A Heterogeneous Particle Swarm Optimizer
Engelbrecht, AP. 2011.
accepted for IEEE Swarm Intelligence Symposium

Not Available For Download.

Abstract:

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Comparison of Trade Decision Strategies in an Equity Market GA Trader
Nicholls, JF. Malan, KM. Engelbrecht, AP. 2011.
accepted for IEEE Symposium on Computational Intelligence for Financial Engineering & Economics

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Abstract:

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Coevolutionary Particle Swarm Optimization for Evolving Trend Reversal Indicators
Papacostantis, E. Engelbrecht, AP. 2011.
accepted for IEEE Symposium on Computational Intelligence for Financial Engineering & Economics

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Abstract:

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Binary Artificial Bee Colony Optimization
Pampara, G. Engelbrecht, AP. 2011.
accepted for IEEE Swarm Intelligence Symposium

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Abstract:

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Heterogeneous Particle Swarms in Dynamic Environments
Leonard, BJ. Engelbrecht, AP. van Wyk, AB. 2011.
accepted for IEEE Swarm Intelligence Symposium

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Abstract:

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Lamba-Gamma Learning with Feedforward Neural Networks using Particle Swarm Optimization
van Wyk, AB. Engelbrecht, AP. 2011.
accepted for IEEE Swarm Intelligence Symposium

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Abstract:

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Self-Adaptive Competitive Differential Evolution for Dynamic Environments
du Plessis, MC. Engelbrecht, AP. 2011.
accepted for Symposium on Differential Evolution

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Abstract:

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Dynamic Load Balancing Inspired by Division of Labour in Ant Colonies
Klazar, R. Engelbrecht, AP. 2011.
accepted for IEEE Swarm Intelligence Symposium

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Abstract:

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