Evolutionary population dynamics and multi-objective optimisation problems

Andrew Lewis, Sanaz Mostaghim, Marcus Randall

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

6 Citations (Scopus)

Abstract

Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective problems have also been the subject of intensive investigation and development recently for metaheuristic search algorithms such as ant colony optimisation, particle swarm optimisation and extremal optimisation. In this chapter, a unifying framework called evolutionary programming dynamics (EPD) is examined. Using underlying concepts of self organised criticality and evolutionary programming, it can be applied to many optimisation algorithms as a controlling metaheuristic, to improve performance and results. We show this to be effective for both continuous and combinatorial problems.

Original languageEnglish
Title of host publicationMulti-Objective Optimization in Computational Intelligence: Theory and Practice
PublisherIGI Global
Pages185-206
Number of pages22
ISBN (Print)9781599044989
DOIs
Publication statusPublished - 2008

Fingerprint

Population dynamics
Multiobjective optimization
Evolutionary algorithms
Ant colony optimization
Particle swarm optimization (PSO)
Industry

Cite this

Lewis, A., Mostaghim, S., & Randall, M. (2008). Evolutionary population dynamics and multi-objective optimisation problems. In Multi-Objective Optimization in Computational Intelligence: Theory and Practice (pp. 185-206). IGI Global. https://doi.org/10.4018/978-1-59904-498-9.ch007
Lewis, Andrew ; Mostaghim, Sanaz ; Randall, Marcus. / Evolutionary population dynamics and multi-objective optimisation problems. Multi-Objective Optimization in Computational Intelligence: Theory and Practice. IGI Global, 2008. pp. 185-206
@inbook{4421219971c64044a1b6d3be52ed86fa,
title = "Evolutionary population dynamics and multi-objective optimisation problems",
abstract = "Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective problems have also been the subject of intensive investigation and development recently for metaheuristic search algorithms such as ant colony optimisation, particle swarm optimisation and extremal optimisation. In this chapter, a unifying framework called evolutionary programming dynamics (EPD) is examined. Using underlying concepts of self organised criticality and evolutionary programming, it can be applied to many optimisation algorithms as a controlling metaheuristic, to improve performance and results. We show this to be effective for both continuous and combinatorial problems.",
author = "Andrew Lewis and Sanaz Mostaghim and Marcus Randall",
year = "2008",
doi = "10.4018/978-1-59904-498-9.ch007",
language = "English",
isbn = "9781599044989",
pages = "185--206",
booktitle = "Multi-Objective Optimization in Computational Intelligence: Theory and Practice",
publisher = "IGI Global",
address = "United States",

}

Lewis, A, Mostaghim, S & Randall, M 2008, Evolutionary population dynamics and multi-objective optimisation problems. in Multi-Objective Optimization in Computational Intelligence: Theory and Practice. IGI Global, pp. 185-206. https://doi.org/10.4018/978-1-59904-498-9.ch007

Evolutionary population dynamics and multi-objective optimisation problems. / Lewis, Andrew; Mostaghim, Sanaz; Randall, Marcus.

Multi-Objective Optimization in Computational Intelligence: Theory and Practice. IGI Global, 2008. p. 185-206.

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

TY - CHAP

T1 - Evolutionary population dynamics and multi-objective optimisation problems

AU - Lewis, Andrew

AU - Mostaghim, Sanaz

AU - Randall, Marcus

PY - 2008

Y1 - 2008

N2 - Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective problems have also been the subject of intensive investigation and development recently for metaheuristic search algorithms such as ant colony optimisation, particle swarm optimisation and extremal optimisation. In this chapter, a unifying framework called evolutionary programming dynamics (EPD) is examined. Using underlying concepts of self organised criticality and evolutionary programming, it can be applied to many optimisation algorithms as a controlling metaheuristic, to improve performance and results. We show this to be effective for both continuous and combinatorial problems.

AB - Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective problems have also been the subject of intensive investigation and development recently for metaheuristic search algorithms such as ant colony optimisation, particle swarm optimisation and extremal optimisation. In this chapter, a unifying framework called evolutionary programming dynamics (EPD) is examined. Using underlying concepts of self organised criticality and evolutionary programming, it can be applied to many optimisation algorithms as a controlling metaheuristic, to improve performance and results. We show this to be effective for both continuous and combinatorial problems.

UR - http://www.scopus.com/inward/record.url?scp=84899287599&partnerID=8YFLogxK

U2 - 10.4018/978-1-59904-498-9.ch007

DO - 10.4018/978-1-59904-498-9.ch007

M3 - Chapter

SN - 9781599044989

SP - 185

EP - 206

BT - Multi-Objective Optimization in Computational Intelligence: Theory and Practice

PB - IGI Global

ER -

Lewis A, Mostaghim S, Randall M. Evolutionary population dynamics and multi-objective optimisation problems. In Multi-Objective Optimization in Computational Intelligence: Theory and Practice. IGI Global. 2008. p. 185-206 https://doi.org/10.4018/978-1-59904-498-9.ch007