Evolutionary Diversity Optimization Using Multi-Objective Indicators

被引：26
|
作者：
Neumann, Aneta [1 ]
Gao, Wanru [1 ]
Wagner, Markus [1 ]
Neumann, Frank [1 ]
机构：
[1] Univ Adelaide, Sch Comp Sci, Adelaide, SA, Australia
来源：
PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19) | 2019年
基金：
澳大利亚研究理事会;
关键词：
Diversity; evolutionary algorithms; features; ALGORITHM;
D O I：
10.1145/3321707.3321796
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Evolutionary diversity optimization aims to compute a set of solutions that are diverse in the search space or instance feature space, and where all solutions meet a given quality criterion. With this paper, we bridge the areas of evolutionary diversity optimization and evolutionary multi-objective optimization. We show how popular indicators frequently used in the area of multi-objective optimization can be used for evolutionary diversity optimization. Our experimental investigations for evolving diverse sets of TSP instances and images according to various features show that two of the most prominent multi-objective indicators, namely the hypervolume indicator and the inverted generational distance, provide excellent results in terms of visualization and various diversity indicators.
引用
收藏
页码：837 / 845
页数：9
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