Novelty search for global optimization

被引：42

作者：

Fister, Iztok ^{[1
,2
]}

Iglesias, Andres ^{[2
]}

Galvez, Akemi ^{[2
]}

Del Ser, Javier ^{[3
,4
,5
]}

Osaba, Eneko ^{[5
]}

Fister, Iztok, Jr. ^{[1
]}

Perc, Matjaz ^{[6
,7
,8
]}

Slavinec, Mitja ^{[6
]}

机构：

[1] Univ Maribor, Fac Elect Engn & Comp Sci, SI-2000 Maribor, Slovenia

[2] Univ Cantabria, Ave Castros S-N, E-39005 Santander, Spain

[3] Univ Basque Country, UPV EHU, Bilbao, Spain

[4] BCAM, Bilbao, Spain

[5] TECNALIA Res & Innovat, Derio 48160, Spain

[6] Univ Maribor, Fac Nat Sci & Math, Koroska Cesta 160, SI-2000 Maribor, Slovenia

[7] Univ Maribor, CAMTP, Mladinska 3, SI-2000 Maribor, Slovenia

[8] Complex Sci Hub Vienna, Josefstadterstr 39, A-1080 Vienna, Austria

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2019年 / 347卷

基金：

欧盟地平线“2020”;

关键词：

Novelty search; Differential evolution; Swarm intelligence; Evolutionary robotics; Artificial life; STATISTICAL COMPARISONS; DIFFERENTIAL EVOLUTION; CLASSIFIERS; TESTS;

D O I：

10.1016/j.amc.2018.11.052

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Novelty search is a tool in evolutionary and swarm robotics for maintaining the diversity of population needed for continuous robotic operation. It enables nature-inspired algorithms to evaluate solutions on the basis of the distance to their k-nearest neighbors in the search space. Besides this, the fitness function represents an additional measure for evaluating the solution, with the purpose of preserving the so-named novelty solutions into the next generation. In this study, a differential evolution was hybridized with novelty search. The differential evolution is a well-known algorithm for global optimization, which is applied to improve the results obtained by the other solvers on the CEC-14 benchmark function suite. Furthermore, functions of different dimensions were taken into consideration, and the influence of the various novelty search parameters was analyzed. The results of experiments show a great potential for using novelty search in global optimization. (C) 2018 Elsevier Inc. All rights reserved.

引用

页码：865 / 881

页数：17

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