Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Speciation as automatic categorical modularization
IEEE Transactions on Evolutionary Computation
On the role of population size and niche radius in fitness sharing
IEEE Transactions on Evolutionary Computation
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
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This work deals with the problem of automatically obtaining ANNs that cooperate in modelling of complex functions. We propose an algorithm where the combination of networks takes place at the phenotypic operational level. Thus, we evolve a population of networks that are automatically classified into different species depending on the performance of their phenotype, and individuals of each species cooperate forming a group to obtain a complex output. The components that make up the groups are basic ANNs (primitives) and could be reused in other search processes as seeds or could be combined to generate new solutions. The magnitude that reflects the difference between ANNs is their affinity vector, which must be automatically created and modified. The main objective of this approach is to model complex functions such as environment models in robotics or multidimensional signals.