research-article

Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights

Authors:

Andrew M. SuttonAuthors Info & Claims

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

Pages 1187 - 1194

https://doi.org/10.1145/3449639.3459381

Published: 26 June 2021 Publication History

Abstract

Addressing a complex real-world optimization problem is a challenging task. The chance-constrained knapsack problem with correlated uniform weights plays an important role in the case where dependent stochastic components are considered. We perform runtime analysis of a randomized search algorithm (RSA) and a basic evolutionary algorithm (EA) for the chance-constrained knapsack problem with correlated uniform weights. We prove bounds for both algorithms for producing a feasible solution. Furthermore, we investigate the behaviour of the algorithms and carry out analyses on two settings: uniform profit value and the setting in which every group shares an arbitrary profit profile. We provide insight into the structure of these problems and show how the weight correlations and the different profit profiles influence the runtime behavior of both algorithms in the chance-constrained setting.

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Doerr BLi XHandl J(2024)A Gentle Introduction to Theory (for Non-Theoreticians)Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3648402(800-829)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3648402
Hewa Pathiranage INeumann FAntipov DNeumann ALi XHandl J(2024)Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack ProblemProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654067(520-528)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654067
Pathirage Don TNeumann ANeumann FLi XHandl J(2024)The Chance Constrained Travelling Thief Problem: Problem Formulations and AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654014(214-222)Online publication date: 14-Jul-2024
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Index Terms

Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights
1. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Random search heuristics
  2. Theory and algorithms for application domains
    1. Theory of randomized search heuristics

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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

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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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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 1,532 of 4,029 submissions, 38%

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Cited By

Doerr BLi XHandl J(2024)A Gentle Introduction to Theory (for Non-Theoreticians)Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3648402(800-829)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3648402
Hewa Pathiranage INeumann FAntipov DNeumann ALi XHandl J(2024)Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack ProblemProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654067(520-528)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654067
Pathirage Don TNeumann ANeumann FLi XHandl J(2024)The Chance Constrained Travelling Thief Problem: Problem Formulations and AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654014(214-222)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654014
Perera KNeumann FNeumann A(2024)Multi-objective Evolutionary Approaches for the Knapsack Problem with Stochastic ProfitsParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_8(116-132)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70055-2_8
Doerr BSilva SPaquete L(2023)A Gentle Introduction to Theory (for Non-Theoreticians)Proceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3595042(946-975)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3595042
Friedrich TKötzing TNeumann ANeumann FRadhakrishnan ASilva SPaquete L(2023)Analysis of (1+1) EA on LeadingOnes with ConstraintsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590453(1584-1592)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590453
Doerr BWagner M(2022)A gentle introduction to theory (for non-theoreticians)Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533628(890-921)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533628
Xia XHuang ZPeng XChen ZXiang Y(2022)Performance analysis of evolutionary algorithm for the maximum internal spanning tree problemThe Journal of Supercomputing10.1007/s11227-022-04342-578:9(11949-11973)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11227-022-04342-5
Doerr BNeumann F(2021)A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete OptimizationACM Transactions on Evolutionary Learning and Optimization10.1145/34723041:4(1-43)Online publication date: 13-Oct-2021
https://dl.acm.org/doi/10.1145/3472304

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