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Maximizing population diversity in single-objective optimization

Authors:

Lothar ThieleAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 641 - 648

https://doi.org/10.1145/2001576.2001665

Published: 12 July 2011 Publication History

Abstract

Typically, optimization attempts to find a solution which minimizes the given objective function. But often, it might also be useful to obtain a set of structurally very diverse solutions which all have acceptable objective values. With such a set, a decision maker would be given a choice of solutions to select from. In addition, he can learn about the optimization problem at hand by inspecting the diverse close-to-optimal solutions. This paper proposes NOAH, an evolutionary algorithm which solves a mixed multi-objective problem: Determine a maximally diverse set of solutions whose objective values are below a provided objective barrier. It does so by iteratively switching between objective value and set-diversity optimization while automatically adapting a constraint on the objective value until it reaches the barrier. Tests on an nk-Landscapes problem and a 3-Sat problem as well as on a more realistic bridge construction problem show that the algorithm is able to produce high quality solutions with a significantly higher structural diversity than standard evolutionary algorithms.

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Cited By

Hashimoto S(2025)Optimization Problem Formulations for Overcoming Difficulties in Real-World ProjectsAlgorithmic Foundations for Social Advancement10.1007/978-981-96-0668-9_9(147-153)Online publication date: 8-Mar-2025
https://doi.org/10.1007/978-981-96-0668-9_9
Nikfarjam ANeumann ANeumann F(2024)On the Use of Quality Diversity Algorithms for the Travelling Thief ProblemACM Transactions on Evolutionary Learning and Optimization10.1145/3641109Online publication date: 17-Jan-2024
https://doi.org/10.1145/3641109
Schmidbauer MOpris ABossek JNeumann FSudholt DLi XHandl J(2024)Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean ConjunctionsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654160(1614-1622)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654160
Show More Cited By

Index Terms

Maximizing population diversity in single-objective optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Published In

cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 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

New York, NY, United States

Publication History

Published: 12 July 2011

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diversity in decision space

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Cited By

Hashimoto S(2025)Optimization Problem Formulations for Overcoming Difficulties in Real-World ProjectsAlgorithmic Foundations for Social Advancement10.1007/978-981-96-0668-9_9(147-153)Online publication date: 8-Mar-2025
https://doi.org/10.1007/978-981-96-0668-9_9
Nikfarjam ANeumann ANeumann F(2024)On the Use of Quality Diversity Algorithms for the Travelling Thief ProblemACM Transactions on Evolutionary Learning and Optimization10.1145/3641109Online publication date: 17-Jan-2024
https://doi.org/10.1145/3641109
Schmidbauer MOpris ABossek JNeumann FSudholt DLi XHandl J(2024)Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean ConjunctionsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654160(1614-1622)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654160
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
Zanartu FTreude CWagner MLi XHandl J(2024)Socialz: Multi-Feature Social Fuzz TestingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654033(1445-1453)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654033
Bossek JSudholt D(2024)Runtime Analysis of Quality Diversity AlgorithmsAlgorithmica10.1007/s00453-024-01254-z86:10(3252-3283)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00453-024-01254-z
Do AGuo MNeumann ANeumann F(2024)Evolutionary Multi-objective Diversity OptimizationParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70085-9_8(117-134)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70085-9_8
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
Gadea Harder JNeumann ANeumann F(2024)Analysis of Evolutionary Diversity Optimisation for the Maximum Matching ProblemParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_10(149-165)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70071-2_10
Preuß ORook JTrautmann H(2024)On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation ProblemsApplications of Evolutionary Computation10.1007/978-3-031-56852-7_20(305-321)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_20
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