交叉表示遗传规划:基于树和线性表示的案例研究。

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhixing Huang, Yi Mei, Fangfang Zhang, Mengjie Zhang, Wolfgang Banzhaf
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引用次数: 0

摘要

现有的遗传规划(GP)方法通常基于一定的表示,如基于树的或线性的表示。这些表示显示了不同领域的各种优点和缺点。然而,由于GP的表示和适应度景观之间的复杂关系,很难直观地确定哪种GP表示最适合解决某一问题。同时具有多个表示的进化程序(或模型)可以选择搜索不同的适应度景观,因为表示与本质上定义适应度景观的搜索空间高度相关。充分利用不同GP个体表征之间的潜在协同效应,有助于GP寻求更好的解决方案。然而,现有的GP文献很少研究多重表征的同时有效演化。为了填补这一空白,本文提出了一种基于树表示和线性表示的交叉表示GP算法。此外,我们开发了一种新的交叉表示交叉算子来利用基于树的表示和线性表示之间的相互作用。实证结果表明,在基本树表示和线性表示之间成功地导航所学知识,提高了仅基于树表示或线性表示的GP在解决符号回归和动态作业车间调度问题中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations.

Existing genetic programming (GP) methods are typically designed based on a certain representation, such as tree-based or linear representations. These representations show various pros and cons in different domains. However, due to the complicated relationships among representation and fitness landscapes of GP, it is hard to intuitively determine which GP representation is the most suitable for solving a certain problem. Evolving programs (or models) with multiple representations simultaneously can alternatively search on different fitness landscapes since representations are highly related to the search space that essentially defines the fitness landscape. Fully using the latent synergies among different GP individual representations might be helpful for GP to search for better solutions. However, existing GP literature rarely investigates the simultaneous effective evolution of multiple representations. To fill this gap, this paper proposes a cross-representation GP algorithm based on tree-based and linear representations, which are two commonly used GP representations. In addition, we develop a new cross-representation crossover operator to harness the interplay between tree-based and linear representations. Empirical results show that navigating the learned knowledge between basic tree-based and linear representations successfully improves the effectiveness of GP with solely tree-based or linear representation in solving symbolic regression and dynamic job shop scheduling problems.

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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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