On the Effect of Populations in Evolutionary Multi-Objective Optimisation-Reference-Cited by-同舟云学术

On the Effect of Populations in Evolutionary Multi-Objective Optimisation

Published:2010-09 Issue:3 Volume:18 Page:335-356
ISSN:1063-6560
Container-title:Evolutionary Computation
language:en
Short-container-title:Evolutionary Computation

Author:

Giel Oliver¹,Lehre Per Kristian²

Affiliation:

1. Fakultät für Informatik, LS 2, Technische Universität Dortmund, Germany.

2. School of Computer Science, The University of Birmingham, United Kingdom.

Abstract

Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present two simple bi-objective problems which emphasise when populations are needed. Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm simple evolutionary multi-objective optimiser (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA is successful and all other algorithms fail.

Publisher

MIT Press - Journals

Subject

Computational Mathematics

Link

https://www.mitpressjournals.org/doi/pdf/10.1162/EVCO_a_00013

Reference13 articles.

1. On the Effects of Adding Objectives to Plateau Functions

2. On the analysis of the (1+1) evolutionary algorithm

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5. On the Choice of the Offspring Population Size in Evolutionary Algorithms

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