Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/109118
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Type: Conference paper
Title: Runtime analysis for maximizing population diversity in single-objective optimization
Author: Gao, W.
Neumann, F.
Citation: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014 / Igel, C. (ed./s), pp.777-784
Publisher: Association for Computing Machinery
Issue Date: 2014
ISBN: 9781450326629
Conference Name: 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO'14) (12 Jul 2014 - 16 Jul 2014 : Vancouver, Canada)
Editor: Igel, C.
Statement of
Responsibility: 
Wanru Gao, Frank Neumann
Abstract: Recently Ulrich and Thiele [14] have introduced evolutionary algorithms for the mixed multi-objective problem of maximizing fitness as well as diversity in the decision space. Such an approach allows to generate a diverse set of solutions which are all of good quality. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for maximizing the diversity in a population that contains several solutions of high quality. We study how evolutionary algorithms maximize the diversity of a population where each individual has to have fitness beyond a given threshold value. We present a first runtime analysis in this area and study the classical problems called OneMax and LeadingOnes. Our results give first rigorous insights on how evolutionary algorithms can be used to produce a maximal diverse set of solutions in which all solutions have quality above a certain threshold value.
Keywords: Runtime analysis, single objective optimization, diversity optimization
Rights: Copyright 2014 ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
DOI: 10.1145/2576768.2598251
Grant ID: http://purl.org/au-research/grants/arc/DP130104395
http://purl.org/au-research/grants/arc/DP140103400
Published version: http://dl.acm.org/citation.cfm?id=2576768
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Computer Science publications

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