Probabilistic Lexicase Selection
Probabilistic Lexicase Selection
复制标题
概率词库选择
DOI：
10.1145/3583131.3590375
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Spector, Lee
中科院分区：
文献类型：
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作者：
Ding, Li;Pantridge, Edward;Spector, Lee
词典选择是遗传编程中广泛使用的父选择算法，以其在程序合成、符号回归和机器学习等各种任务领域的成功而闻名。由于其非参数和递归性质，计算每个个体被词典选择选择的概率已被证明是NP难问题，这阻碍了对算法的深入理论理解和实际改进。在这项工作中，我们引入概率词典选择（plexicase选择），一种新的父选择算法，有效地近似词典选择的概率分布。我们的方法不仅表现出上级的解决问题的能力，作为一个语义感知的选择方法，但也受益于有一个概率表示的选择过程，以提高效率和灵活性。在遗传编程中的两个流行领域进行实验：程序合成和符号回归，使用标准基准测试，包括PSB和SRBench。实验结果表明，复合体选择算法在求解问题时的性能与词典选择算法相当，并且在计算效率上明显优于词典选择算法.
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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DOI：
--
发表时间：
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期刊：
Annual Conference on Genetic and Evolutionary Computation
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1145/3319619.3326900
发表时间：
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期刊：
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference
影响因子：
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作者：
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通讯作者：
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影响因子：
1.6
作者：
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通讯作者：
Pearson, Karl
DOI：
10.3390/e25010093
发表时间：
2023-01-03
期刊：
Entropy (Basel, Switzerland)
影响因子：
--
作者：
通讯作者：
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