Abstract
In this work, we will use self-organizing feature maps as a method of visualization the sampling of the fitness space considered by the populations of two evolutionary methods, genetic and macroevolutionary algorithms, in a case with a mostly flat fitness landscape and low populations. Macroevolutionary algorithms will allow obtaining better results due to the way in which they handle the exploration-exploitation equilibrium. We test it with different alternatives using the self-organizing maps.
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Becerra, J.A., Santos, J. (2005). Neural Clustering Analysis of Macroevolutionary and Genetic Algorithms in the Evolution of Robot Controllers. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_43
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DOI: https://doi.org/10.1007/11499305_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26319-7
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