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- W4313598366 abstract "Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions. However, creating a down-sample randomly might exclude important cases from the current down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused despite their redundancy. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while also benefiting from reduced per-evaluation costs." @default.
- W4313598366 created "2023-01-06" @default.
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- W4313598366 date "2023-01-04" @default.
- W4313598366 modified "2023-09-26" @default.
- W4313598366 title "Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving" @default.
- W4313598366 doi "https://doi.org/10.48550/arxiv.2301.01488" @default.
- W4313598366 hasPublicationYear "2023" @default.
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