Summary
Schemata theorem is based on the notion that similarities among the individuals of the population of a genetic algorithm are related to the substructures present in the problem being solved. Similarities are currently taken into account in evolutionary computation since the preservation of the diversity in the population is considered very important to allow the identification of the problem structure, as much as to find and maintain several global optima. This work shows an empirical investigation concerning the application of an algorithm which is based on learning from similarities among individuals. Those similarities are shown to reveal information about the problem structure. Empirical evaluation and comparison show the effectiveness and scalability of this new approach when solving multimodal optimization problems, including several instances of graph bisection and a highly multimodal parity function.
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Emmendorfer, L., Pozo, A. (2008). A Clustering-Based Approach for Linkage Learning Applied to Multimodal Optimization. In: Chen, Yp., Lim, MH. (eds) Linkage in Evolutionary Computation. Studies in Computational Intelligence, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85068-7_10
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DOI: https://doi.org/10.1007/978-3-540-85068-7_10
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