局部进化搜索中的若干问题

Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) Pub Date : 1999-07-06 DOI:10.1109/CEC.1999.782012

H. Voigt

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引用次数: 2

摘要

我们考虑在不使用解析梯度信息的情况下，在R/sup / n/上优化平稳单峰函数的非常简单的问题。目前已有从数学规划到进化算法等多种算法来解决这一问题。我们仔细研究了高级进化策略(GSA, CMA)，进化梯度搜索算法(EGS)，随机记忆局部搜索增强(LSERM)和简单的(1+1)-进化策略。这些方法为不同的测试功能显示了不同的问题解决能力。我们引入了不同的措施，这些措施反映了可能被视为问题难度的某些方面。基于这些措施，可以表征方法的弱点和长处，这可能导致更先进的算法。

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On some difficulties in local evolutionary search

We consider the very simple problem of optimizing a stationary unimodal function over R/sup n/ without using analytical gradient information. There exist numerous algorithms from mathematical programming to evolutionary algorithms for this problem. We have a closer look at advanced evolution strategies (GSA, CMA), the evolutionary gradient search algorithm (EGS), local search enhancement by random memorizing (LSERM), and the simple (1+1)-evolution strategy. These approaches show different problem-solving capabilities for different test functions. We introduce different measures which reflect certain aspects of what might be seen as the problem difficulty. Based on these measures it is possible to characterize the weak and strong points of the approaches which may lead to even more advanced algorithms.

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Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)

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