基于模型的小表达式符号回归遗传规划改进。

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
M Virgolin, T Alderliesten, C Witteveen, P A N Bosman
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引用次数: 49

摘要

基因池最优混合进化算法(gome)是一种基于模型的EA框架,在遗传规划(GP)等多个领域表现良好。与变异盲目作用的传统ea不同,goma学习基因型内相互依赖的模型,即连锁,以估计传播哪种模式。在本文中,我们研究了goma在符号回归(SR)中的作用。我们表明GP群体中基因型分布的不均匀性负偏倚LL,并提出了一种方法来纠正这一点。我们还提出了在使用短暂随机常数时改进LL的方法。此外,我们采用了交错运行方案,以减轻调整种群大小(LL的关键参数)到sr的负担。我们在10个真实数据集上运行实验,严格限制解决方案的大小,以实现可解释性。我们发现,新方法优于标准方法,并且goma优于传统GP和语义GP。我们还发现,由goma演化出的小解与调优决策树具有竞争力,这使得goma成为一种很有前途的SR新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions.

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, that is, the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.

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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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