Schema processing, proportional selection, and the misallocation of trials in genetic algorithms

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2000-02-01 DOI:10.1016/S0020-0255(99)00118-8

David B. Fogel , Adam Ghozeil

引用次数: 10

Abstract

Traditional selection in genetic algorithms has relied on reproduction in proportion to observed fitness. There has been recent interest in assessing the result of proportional selection on schemata in the presence of random effects (e.g., noisy evaluation of solutions). The analysis presented here indicates, in contrast with previous literature, that the introduction of noise to the evaluation of solutions can change the expected sampling of schemata, even when the noise is zero mean. This effect is examined in a variety of settings. Unfortunately, this “misallocation of trials” can also result simply from random initialization of a population.

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图式处理，比例选择，以及遗传算法中试验的错配

遗传算法中的传统选择依赖于繁殖与观察到的适应度成比例。在随机效应(例如，解决方案的噪声评价)存在的情况下，对图式的比例选择结果的评估最近引起了人们的兴趣。本文的分析表明，与之前的文献相反，在解的评估中引入噪声可以改变模式的期望采样，即使噪声为零均值。这种效应在不同的环境中得到检验。不幸的是，这种“试验的错误分配”也可能仅仅是由总体的随机初始化造成的。

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

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.