Dynamic selection of evolutionary algorithm operators based on online learning and fitness landscape metrics

被引：15

作者：

Consoli, Pietro A. ^{[1
]}

Minku, Leandro L. ^{[1
]}

Cercia, Xin Yao ^{[1
]}

机构：

[1] School of Computer Science, University of Birmingham Birmingham, West Midlands,B15 2TT, United Kingdom

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8886卷

基金：

英国工程与自然科学研究理事会;

关键词：

Dynamics - Learning algorithms - E-learning;

D O I：

10.1007/978-3-319-13563-2_31

中图分类号：

TP181 [自动推理、机器学习];

学科分类号：

摘要：

Self-adaptive mechanisms for the identification of the most suitable variation operator in Evolutionary meta-heuristics rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel Adaptive Operator Selection mechanism which uses a set of four Fitness Landscape Analysis techniques and an online learning algorithm, Dynamic Weighted Majority, to provide more detailed informations about the search space in order to better determine the most suitable crossover operator on a set of Capacitated Arc Routing Problem (CARP) instances. Extensive comparison with a state of the art approach has proved that this technique is able to produce comparable results on the set of benchmark problems. © Springer International Publishing Switzerland 2014.

引用

页码：359 / 370