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Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice

机译：嘈杂环境中的进化算法：理论问题和实践指南

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摘要

This paper is devoted to the effects of fitness noise in evolutionary algorithms (EAs). After a short introduction to the history of this research field, the performance of genetic algorithms (GAs) and evolution strategies (ESs) on the hyper-sphere test function is evaluated. It will be shown that the main effects of noise - the decrease of convergence velocity and the residual location error R_∞- are observed in both GAs and ESs. Different methods for improving the performance are presented and hypotheses on their working mechanisms are discussed. The method of rescaled mutations is analyzed in depth for the (l, λ)-ES on the sphere model. It is shown that this method needs advanced self-adaptation (SA) techniques in order to take advantage of the theoretically predicted performance gain. The troubles with current self adaptation techniques are discussed and directions for further research will be worked out.
机译：本文致力于适应性噪声在进化算法（EA）中的影响。在简要介绍该研究领域的历史之后，对遗传算法（GA）和进化策略（ESs）在超球面测试功能上的性能进行了评估。结果表明，在GA和ES中都观察到了噪声的主要影响-收敛速度的降低和残余位置误差R_∞-。提出了提高性能的不同方法，并讨论了其工作机制的假设。在球形模型上针对（l，λ）-ES深入分析了重新缩放的突变方法。结果表明，该方法需要先进的自适应（SA）技术，以便利用理论上预测的性能增益。讨论了当前自适应技术的麻烦，并将为进一步的研究指明方向。

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