A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
The evolution of evolvability in genetic programming
Advances in genetic programming
Topology representing networks
Neural Networks
Self-Organizing Maps
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Self-organizing potential field network: a new optimization algorithm
IEEE Transactions on Neural Networks
Perspectives of self-adapted self-organizing clustering in organic computing
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
International Journal of Swarm Intelligence Research
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In evolution strategies with neighborhood attraction (EN) the concepts of neighborhood cooperativeness and learning rules known from neural maps are transferred onto the individuals of evolution strategies. A previous approach, which utilized a neighborhood relationship adapted from self-organizing maps (SOM), appeared to perform as well as or even better than comparable conventional evolution strategies on a variety of common test functions. In this contribution, an EN with a new neighborhood relationship and learning rule based on the idea of neural gas is introduced. Its performance is compared to the SOM-like approach, using the same test functions. It is shown that the neural gas approach is considerably faster in finding the optimum than the SOM approach, although the latter seems to be more robust for multimodal problems.