research-article

Breeding diverse packings for the knapsack problem by means of diversity-tailored evolutionary algorithms

Authors:

Frank NeumannAuthors Info & Claims

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 556 - 564

https://doi.org/10.1145/3449639.3459364

Published: 26 June 2021 Publication History

Abstract

In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least (1 - ε) · OPT, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple (μ + 1)-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited.

References

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Cited By

Nikfarjam ANeumann ANeumann F(2024)On the Use of Quality Diversity Algorithms for the Travelling Thief ProblemACM Transactions on Evolutionary Learning and Optimization10.1145/36411094:2(1-22)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/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
Show More Cited By

Index Terms

Breeding diverse packings for the knapsack problem by means of diversity-tailored evolutionary algorithms
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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cover image ACM Conferences

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference

June 2021

1219 pages

ISBN:9781450383509

DOI:10.1145/3449639

Editor:
Francisco Chicano
University of Malaga
,
General Chair:
Krzysztof Krawiec
Poznan University of Technology

Copyright © 2021 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 26 June 2021

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Australian Research Council

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GECCO '21

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SIGEVO

GECCO '21: Genetic and Evolutionary Computation Conference

July 10 - 14, 2021

Lille, France

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

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Cited By

Nikfarjam ANeumann ANeumann F(2024)On the Use of Quality Diversity Algorithms for the Travelling Thief ProblemACM Transactions on Evolutionary Learning and Optimization10.1145/36411094:2(1-22)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/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
Ngo HGuo MNguyen HLi XHandl J(2024)Optimizing Cyber Response Time on Temporal Active Directory Networks Using DecoysProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654035(1309-1317)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654035
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
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
Antipov DNeumann ANeumann F(2023)Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMaxProceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms10.1145/3594805.3607135(3-14)Online publication date: 30-Aug-2023
https://dl.acm.org/doi/10.1145/3594805.3607135
Nikfarjam ARothenberger RNeumann FFriedrich TSilva SPaquete L(2023)Evolutionary Diversity Optimisation in Constructing Satisfying AssignmentsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590517(938-945)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590517
Do AGuo MNeumann ANeumann F(2022)Analysis of Evolutionary Diversity Optimization for Permutation ProblemsACM Transactions on Evolutionary Learning and Optimization10.1145/35619742:3(1-27)Online publication date: 19-Oct-2022
https://dl.acm.org/doi/10.1145/3561974
Nikfarjam AViet Do ANeumann F(2022)Analysis of Quality Diversity Algorithms for the Knapsack ProblemParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14721-0_29(413-427)Online publication date: 10-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-14721-0_29
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