Reinforcement learning for online control of evolutionary algorithms

被引：0

作者：

Eiben, A. E. ^{[1
]}

Horvath, Mark ^{[1
]}

Kowalczyk, Wojtek ^{[1
]}

Schut, Martijn C. ^{[1
]}

机构：

[1] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands

来源：

ENGINEERING SELF-ORGANISING SYSTEMS | 2007年 / 4335卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We evaluate this approach experimentally on a range of fitness landscapes with varying degrees of ruggedness. The results show that EA calibrated by the RL-based approach outperforms a benchmark EA.

引用

页码：151 / +

页数：3

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