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An Advanced Evolution Should Not Repeat its Past Errors

机译：高级进化不应重复其过去的错误

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A safe control of genetic evolution consists in preventing past errors of evolution from being repeated. This could be done by keeping track of the history of evolution, but maintaining and exploiting the complete history is intractable.rnThis paper investigates the use of machine learning (ML), in order to extract manageable information from this history. More precisely, induction from examples of past trials and errors provides rules discriminating errors from successful trials. Such rules allow to a priori estimate the desirability .of future trials; this knowledge can support powerful control strategies.rnSeveral strategies of ML-based control are applied to a genetic algorithm, and tested on the Royal Road, a GA-deceptive, and a combinatorial optimization problem. Comparing mutation control with crossover control yields unexpected results.

机译：对遗传进化的安全控制在于防止重复过去的进化错误。可以通过跟踪演化历史来完成，但是维护和利用完整历史是很棘手的。本文研究了机器学习（ML）的用途，以便从该历史中提取可管理的信息。更准确地说，从过去的试验和错误的示例中得出的归纳提供了将错误与成功的试验区分开的规则。这样的规则可以事先估计未来试验的可取性。这些基于知识的控制可以支持强大的控制策略。几种基于ML的控制策略已应用于遗传算法，并在Royal Road上经过了GA欺骗和组合优化问题的测试。将突变控制与交叉控制进行比较会产生意想不到的结果。

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《Machine learning》|1996年|400-408|共9页
会议地点 Bari(IT);Bari(IT)
作者
Caroline Ravise; Michele Sebag;
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作者单位

LMS, CNRS URA 317, Ecole Polytechnique, 91128 Palaiseau, FRANCE LRI, CNRS URA 410, Universite Paris-Sud, 91405 Orsay, FRANCE;

LMS, CNRS URA 317, Ecole Polytechnique, 91128 Palaiseau, FRANCE LRI, CNRS URA 410, Universite Paris-Sud, 91405 Orsay, FRANCE;

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正文语种 eng
中图分类计算机的应用;
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An Advanced Evolution Should Not Repeat its Past Errors

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