Learning a Hidden Markov Model-Based Hyper-heuristic

被引：1

作者：

Van Onsem, Willem ^{[1
]}

Demoen, Bart ^{[1
]}

De Causmaecker, Patrick ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Comp Sci, B-3001 Heverlee, Belgium

来源：

LEARNING AND INTELLIGENT OPTIMIZATION, LION 9 | 2015年 / 8994卷

关键词：

D O I：

10.1007/978-3-319-19084-6_7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A simple model shows how a reasonable update scheme for the probability vector by which a hyper-heuristic chooses the next heuristic leads to neglecting useful mutation heuristics. Empirical evidence supports this on the MAXSAT, TRAVELINGSALESMAN, PERMUTATION-FLOWSHOP and VEHICLEROUTINGPROBLEM problems. A new approach to hyper-heuristics is proposed that addresses this problem by modeling and learning hyper-heuristics by means of a hidden Markov Model. Experiments show that this is a feasible and promising approach.

引用

页码：74 / 88

页数：15

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