The performance of genetic programming relies mostly on population-contained variation. If the population diversity is low then there will be a greater chance of the algorithm being unable to find the global optimum. We present a new method of approximating the genetic similarity between two individuals using ancestry information. We define a new diversity-preserving selection scheme, based on standard tournament selection, which encourages genetically dissimilar individuals to undergo genetic operation. The new method is illustrated by assessing its performance in a well-known problem domain: algebraic symbolic regression. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Fry, R., & Tyrrell, A. (2003). Enhancing the performance of GP using an ancestry-based mate selection scheme. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2724, 1804–1805. https://doi.org/10.1007/3-540-45110-2_73
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