University of Hertfordshire

By the same authors

Enhancing exploration and exploitation of NSGA-II with GP and PDL

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
PublisherSpringer Verlag
Pages349-361
Number of pages13
Volume10385 LNCS
ISBN (Print)9783319618234
DOIs
StateE-pub ahead of print - 24 Jun 2017
Event8th International Conference on Swarm Intelligence, ICSI 2017 - Fukuoka, Japan

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10385 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference8th International Conference on Swarm Intelligence, ICSI 2017
CountryJapan
CityFukuoka
Period27/07/171/08/17

Abstract

In this paper, we show that NSGA-II can be applied to GP and the Process Description Language (PDL) and describe two modifications to NSGA-II. The first modification removes individuals which have the same behaviour from GP populations. It selects for de-duplication by taking the result of each objective fitness function together to make a comparison. NSGA-II is designed to expand its Pareto front of solutions by favouring individuals who have the highest or lowest value (boundary points) in a front, for any objective. The second modification enhances exploitation by preferring individuals who occupy an extreme position for most objective fitness functions. The results show, for the first time, that NSGA-II can be used with PDL and GP to successfully solve a robot control problem and that the suggested modifications offer significant improvements over an algorithm used previously with GP and PDL and unmodified NSGA-II for our test problem.

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