演化微分方程与发育线性遗传规划及表观遗传爬坡

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI:10.1145/2598394.2598491

W. L. Cava, L. Spector, K. Danai, M. Lackner

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引用次数: 12

摘要

本文描述了一种利用发育线性遗传规划(DLGP)求解表观遗传爬山者(EHC)的符号回归问题的方法。我们提出利用EHC优化该基因型的表观遗传特性。然后，表观遗传特征通过与群体的共同进化而遗传。结果表明，EHC通过保持较小的表达程序大小来提高性能。对于某些问题，它产生了更成功的运行，同时在健康评估的数量方面基本上保持成本中立。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing

This paper describes a method of solving the symbolic regression problem using developmental linear genetic programming (DLGP) with an epigenetic hill climber (EHC). We propose the EHC for optimizing the epigenetic properties of the genotype. The epigenetic characteristics are then inherited through coevolution with the population. Results reveal that the EHC improves performance through maintenance of smaller expressed program sizes. For some problems it produces more successful runs while remaining essentially cost-neutral with respect to number of fitness evaluations.

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Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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