Estimation of Distribution Algorithm for Grammar-Guided Genetic Programming.

IF 4.6 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Evolutionary Computation Pub Date : 2024-12-02 DOI:10.1162/evco_a_00345

Pablo Ramos Criado, D Barrios Rolanía, David de la Hoz, Daniel Manrique

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引用次数: 0

Abstract

Genetic variation operators in grammar-guided genetic programming are fundamental to guide the evolutionary process in search and optimization problems. However, they show some limitations, mainly derived from an unbalanced exploration and local-search trade-off. This paper presents an estimation of distribution algorithm for grammar-guided genetic programming to overcome this difficulty and thus increase the performance of the evolutionary algorithm. Our proposal employs an extended dynamic stochastic context-free grammar to encode and calculate the estimation of the distribution of the search space from some promising individuals in the population. Unlike traditional estimation of distribution algorithms, the proposed approach improves exploratory behavior by smoothing the estimated distribution model. Therefore, this algorithm is referred to as SEDA, smoothed estimation of distribution algorithm. Experiments have been conducted to compare overall performance using a typical genetic programming crossover operator, an incremental estimation of distribution algorithm, and the proposed approach after tuning their hyperparameters. These experiments involve challenging problems to test the local search and exploration features of the three evolutionary systems. The results show that grammar-guided genetic programming with SEDA achieves the most accurate solutions with an intermediate convergence speed.

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语法引导遗传编程的分布算法估算。

语法引导遗传编程中的遗传变异算子是引导搜索和优化问题进化过程的基础。然而，它们也存在一些局限性，主要是探索和局部搜索权衡不平衡。本文提出了一种语法引导遗传编程的分布估计算法，以克服这一困难，从而提高进化算法的性能。我们的建议采用一种扩展的动态随机无上下文语法来编码和计算种群中一些有希望的个体对搜索空间分布的估计。与传统的分布估计算法不同，我们提出的方法通过平滑估计分布模型来改善探索行为。因此，这种算法被称为 SEDA，即平滑估计分布算法。通过实验，比较了使用典型遗传编程交叉算子、增量估计分布算法和调整超参数后的拟议方法的整体性能。这些实验涉及具有挑战性的问题，以测试这三种进化系统的局部搜索和探索功能。结果表明，语法引导的遗传编程与 SEDA 以中等收敛速度获得了最准确的解决方案。

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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来源期刊

Evolutionary Computation 工程技术-计算机：理论方法

CiteScore

6.40

自引率

1.50%

发文量

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.