Abstract
We introduce, analyze, and experimentally examine co-solva-bility, an ability of a solution to solve a pair of fitness cases (tests). Based on this concept, we devise a co-solvability fitness function that makes solutions compete for rewards granted for solving pairs of tests, in a way analogous to implicit fitness sharing. We prove that co-solvability fitness function is by definition synergistic and imposes selection pressure which is qualitatively different from that of standard fitness function or implicit fitness sharing. The results of experimental verification on eight genetic programming tasks demonstrate that evolutionary runs driven by co-solvability fitness function usually converge faster to well-performing solutions and are more likely to reach global optima.
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Krawiec, K., Lichocki, P. (2010). Using Co-solvability to Model and Exploit Synergetic Effects in Evolution. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_50
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DOI: https://doi.org/10.1007/978-3-642-15871-1_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15870-4
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