Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Machine Learning
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Adaptive isolation model using data clustering for multimodal function optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An Information Theoretic Approach to Detection of Minority Subsets in Database
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Speciation as automatic categorical modularization
IEEE Transactions on Evolutionary Computation
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
On the role of population size and niche radius in fitness sharing
IEEE Transactions on Evolutionary Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybridization of evolutionary algorithms and local search by means of a clustering method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
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Speciation is an important concept in evolutionary computation. It refers to an enhancements of evolutionary algorithms to generate a set ofdiverse solutions. The concept is studied intensively in the evolutionary design of neural network ensembles. Thediversity and cooperation of individual networks are among the essential criteria of the design.This paper proposes a speciation framework for ensemble design which integratesa collection of new techniques. Its characteristic features are:(a) the population of networks are speciated as such thatthe mutual information between the networks' outputs and genotypic representations is preserved. (b) The ensemble is designed incrementally,upon discovery of a species of networks which enhances the ensembleperformance. (c) Multiple species are evolved andindividual networks are evaluated according to therole of their respective species in the ensemble.This framework provides an implementation of evolutionary algorithm which performs simultaneous single-objective optimizations.The new algorithm is evaluated with a series of classification benchmarks andshows an improvement over other evolutionary training strategiesand a statistical algorithm.