Abstract
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 multi-modal problems.
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Huhse, J., Villmann, T., Merz, P., Zell, A. (2002). Evolution Strategy with Neighborhood Attraction Using a Neural Gas Approach. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_38
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DOI: https://doi.org/10.1007/3-540-45712-7_38
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