Co-evolutionary Diversity Optimisation for the Traveling Thief Problem

被引：1

作者：

Nikfarjam, Adel ^{[1
]}

Neumann, Aneta ^{[1
]}

Bossek, Jakob ^{[2
]}

Neumann, Frank ^{[1
]}

机构：

[1] Univ Adelaide, Sch Comp Sci, Optimisat & Logist, Adelaide, SA, Australia

[2] Rhein Westfal TH Aachen, Dept Comp Sci, AI Methodol, Aachen, Germany

来源：

PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XVII, PPSN 2022, PT I | 2022年 / 13398卷

基金：

澳大利亚研究理事会;

关键词：

Quality diversity; Co-evolutionary algorithms; Evolutionary diversity optimisation; Traveling thief problem;

D O I：

10.1007/978-3-031-14714-2_17

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.

引用

页码：237 / 249

页数：13

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