Probabilistic Lexicase Selection

Li Ding; Edward Pantridge; Lee Spector

doi:10.1145/3583131.3590375

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arXiv · 2305.11681

Probabilistic Lexicase Selection

Li Ding·Edward Pantridge·Lee Spector

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Abstract

Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning. Due to its non-parametric and recursive nature, calculating the probability of each individual being selected by lexicase selection has been proven to be an NP-hard problem, which discourages deeper theoretical understanding and practical improvements to the algorithm. In this work, we introduce probabilistic lexicase selection (plexicase selection), a novel parent selection algorithm that efficiently approximates the probability distribution of lexicase selection. Our method not only demonstrates superior problem-solving capabilities as a semantic-aware selection method, but also benefits from having a probabilistic representation of the selection process for enhanced efficiency and flexibility. Experiments are conducted in two prevalent domains in genetic programming: program synthesis and symbolic regression, using standard benchmarks including PSB and SRBench. The empirical results show that plexicase selection achieves state-of-the-art problem-solving performance that is competitive to the lexicase selection, and significantly outperforms lexicase selection in computation efficiency.

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Li Ding, Edward Pantridge, Lee Spector. 2023-05-19. Probabilistic Lexicase Selection. https://doi.org/10.1145/3583131.3590375

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