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

Standard

Enhancing exploration and exploitation of NSGA-II with GP and PDL. / Shannon, Peter David; Nehaniv, Chrystopher L.; Phon-Amnuaisuk, Somnuk.

Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Vol. 10385 LNCS Springer Verlag, 2017. p. 349-361 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10385 LNCS).

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

Harvard

Shannon, PD, Nehaniv, CL & Phon-Amnuaisuk, S 2017, Enhancing exploration and exploitation of NSGA-II with GP and PDL. in Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. vol. 10385 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10385 LNCS, Springer Verlag, pp. 349-361, 8th International Conference on Swarm Intelligence, ICSI 2017, Fukuoka, Japan, 27-1 August. DOI: 10.1007/978-3-319-61824-1_38

APA

Shannon, P. D., Nehaniv, C. L., & Phon-Amnuaisuk, S. (2017). Enhancing exploration and exploitation of NSGA-II with GP and PDL. In Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings (Vol. 10385 LNCS, pp. 349-361). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10385 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-61824-1_38

Vancouver

Shannon PD, Nehaniv CL, Phon-Amnuaisuk S. Enhancing exploration and exploitation of NSGA-II with GP and PDL. In Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Vol. 10385 LNCS. Springer Verlag. 2017. p. 349-361. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 10.1007/978-3-319-61824-1_38

Author

Shannon, Peter David; Nehaniv, Chrystopher L.; Phon-Amnuaisuk, Somnuk / Enhancing exploration and exploitation of NSGA-II with GP and PDL.

Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Vol. 10385 LNCS Springer Verlag, 2017. p. 349-361 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10385 LNCS).

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

Bibtex

@inbook{9139aad88403453b9110994916f11f4a,
title = "Enhancing exploration and exploitation of NSGA-II with GP and PDL",
keywords = "Exploration and exploitation, Genetic programming, NSGA-II, Process description language",
author = "Shannon, {Peter David} and Nehaniv, {Chrystopher L.} and Somnuk Phon-Amnuaisuk",
year = "2017",
month = "6",
doi = "10.1007/978-3-319-61824-1_38",
isbn = "9783319618234",
volume = "10385 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "349--361",
booktitle = "Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings",
address = "Germany",

}

RIS

TY - CHAP

T1 - Enhancing exploration and exploitation of NSGA-II with GP and PDL

AU - Shannon,Peter David

AU - Nehaniv,Chrystopher L.

AU - Phon-Amnuaisuk,Somnuk

PY - 2017/6/24

Y1 - 2017/6/24

N2 - 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.

AB - 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.

KW - Exploration and exploitation

KW - Genetic programming

KW - NSGA-II

KW - Process description language

UR - http://www.scopus.com/inward/record.url?scp=85026769534&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-61824-1_38

DO - 10.1007/978-3-319-61824-1_38

M3 - Conference contribution

SN - 9783319618234

VL - 10385 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 349

EP - 361

BT - Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings

PB - Springer Verlag

ER -