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Runtime analysis for maximizing population diversity in single-objective optimization

Authors:

Frank NeumannAuthors Info & Claims

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 777 - 784

https://doi.org/10.1145/2576768.2598251

Published: 12 July 2014 Publication History

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 \emph{\OM} and \emph{\LO}. 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.

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Cited By

Antipov DNeumann ANeumann FLi XHandl J(2024)A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMaxProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654082(467-475)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654082
Antipov DNeumann ANeumann FSutton A(2024)Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) ProblemParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_2(19-35)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70071-2_2
Antipov DNeumann ANeumann F(2024)Local Optima in Diversity Optimization: Non-trivial Offspring Population is EssentialParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_12(181-196)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70071-2_12
Show More Cited By

Index Terms

Runtime analysis for maximizing population diversity in single-objective optimization
1. Theory of computation
  1. Design and analysis of algorithms

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Maximizing population diversity in single-objective optimization
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The main goal of diversity optimization is to find a diverse set of solutions which satisfy some lower bound on their fitness. Evolutionary algorithms (EAs) are often used for such tasks, since they are naturally designed to optimize populations ...
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Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) Problem
Parallel Problem Solving from Nature – PPSN XVIII
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Diversity optimization is a class of optimization problems in which we aim to find a diverse set of good solutions. One of the frequently used approaches to solve such problems is to use evolutionary algorithms which evolve a desired diverse ...
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Published In

cover image ACM Conferences

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1478 pages

ISBN:9781450326629

DOI:10.1145/2576768

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

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 [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Published: 12 July 2014

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Antipov DNeumann ANeumann FLi XHandl J(2024)A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMaxProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654082(467-475)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654082
Antipov DNeumann ANeumann FSutton A(2024)Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) ProblemParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_2(19-35)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70071-2_2
Antipov DNeumann ANeumann F(2024)Local Optima in Diversity Optimization: Non-trivial Offspring Population is EssentialParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_12(181-196)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70071-2_12
Bossek JSudholt D(2023)Do Additional Target Points Speed Up Evolutionary Algorithms?Theoretical Computer Science10.1016/j.tcs.2023.113757(113757)Online publication date: Feb-2023
https://doi.org/10.1016/j.tcs.2023.113757
Bossek JNeumann ANeumann FWagner M(2022)Evolutionary diversity optimization for combinatorial optimizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533626(824-842)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533626
Bossek JSudholt DFinck SHellwig MOliveto P(2021)Do additional optima speed up evolutionary algorithms?Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms10.1145/3450218.3477309(1-11)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1145/3450218.3477309
Bossek JNeumann FKrawiec K(2021)Evolutionary diversity optimization and the minimum spanning tree problemProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459363(198-206)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459363
Lai XZhou Y(2020)Analysis of multiobjective evolutionary algorithms on the biobjective traveling salesman problem (1,2)Multimedia Tools and Applications10.1007/s11042-020-09399-zOnline publication date: 17-Aug-2020
https://doi.org/10.1007/s11042-020-09399-z
Maqbool FRazzaq SLehmann JJabeen H(2019)Scalable Distributed Genetic Algorithm Using Apache Spark (S-GA)Intelligent Computing Theories and Application10.1007/978-3-030-26763-6_41(424-435)Online publication date: 24-Jul-2019
https://doi.org/10.1007/978-3-030-26763-6_41
Gao WPourhassan MRoostapour VNeumann F(2019)Runtime Analysis of Evolutionary Multi-objective Algorithms Optimising the Degree and Diameter of Spanning TreesEvolutionary Multi-Criterion Optimization10.1007/978-3-030-12598-1_40(504-515)Online publication date: 10-Mar-2019
https://dl.acm.org/doi/10.1007/978-3-030-12598-1_40
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