research-article

On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint

Authors:

Hans-Georg Beyer,

Steffen FinckAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 16, Issue 4

Pages 578 - 596

https://doi.org/10.1109/TEVC.2011.2169967

Published: 01 August 2012 Publication History

Abstract

This paper describes the algorithm's engineering of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving a mixed linear/nonlinear constrained optimization problem arising in portfolio optimization. While the feasible solution space is defined by the (probabilistic) simplex, the nonlinearity comes in by a cardinality constraint bounding the number of linear inequalities violated. This gives rise to a nonconvex optimization problem. The design is based on the CMSA-ES and relies on three specific techniques to fulfill the different constraints. The resulting algorithm is then thoroughly tested on a data set derived from time series data of the Dow Jones Index.

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Hellwig MFinck S(2023)Joining Emission Data from Diverse Economic Activity Taxonomies with Evolution StrategiesMachine Learning, Optimization, and Data Science10.1007/978-3-031-53969-5_31(415-429)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-53969-5_31
Nomura MSakai NFukushima NOno I(2021)Distance-weighted Exponential Natural Evolution Strategy for Implicitly Constrained Black-Box Function Optimization2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504865(1099-1106)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1109/CEC45853.2021.9504865
Spettel PBeyer H(2020)Analysis of the (μ/μI,λ)-CSA-ES with Repair by Projection Applied to a Conically Constrained ProblemEvolutionary Computation10.1162/evco_a_0026128:3(463-488)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1162/evco_a_00261
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On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint
1. Information systems
  1. Data management systems
    1. Database administration
2. Theory of computation
  1. Design and analysis of algorithms

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cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 16, Issue 4

August 2012

148 pages

ISSN:1089-778X

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IEEE Press

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Published: 01 August 2012

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Hellwig MFinck S(2023)Joining Emission Data from Diverse Economic Activity Taxonomies with Evolution StrategiesMachine Learning, Optimization, and Data Science10.1007/978-3-031-53969-5_31(415-429)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-53969-5_31
Nomura MSakai NFukushima NOno I(2021)Distance-weighted Exponential Natural Evolution Strategy for Implicitly Constrained Black-Box Function Optimization2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504865(1099-1106)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1109/CEC45853.2021.9504865
Spettel PBeyer H(2020)Analysis of the (μ/μI,λ)-CSA-ES with Repair by Projection Applied to a Conically Constrained ProblemEvolutionary Computation10.1162/evco_a_0026128:3(463-488)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1162/evco_a_00261
Spettel PBeyer H(2019)A multi-recombinative active matrix adaptation evolution strategy for constrained optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-03736-z23:16(6847-6869)Online publication date: 1-Aug-2019
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Hellwig MBeyer H(2018)A Matrix Adaptation Evolution Strategy for Constrained Real-Parameter Optimization2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477950(1-8)Online publication date: 8-Jul-2018
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Liu B(2017)Posterior exploration based sequential Monte Carlo for global optimizationJournal of Global Optimization10.1007/s10898-017-0543-869:4(847-868)Online publication date: 1-Dec-2017
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Hellwig MArnold D(2016)Comparison of constraint-handling mechanisms for the 1,λ-es on a simple constrained problemEvolutionary Computation10.1162/EVCO_a_0013924:1(1-23)Online publication date: 1-Mar-2016
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Maesani AIacca GFloreano D(2016)Memetic Viability Evolution for Constrained OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.242829220:1(125-144)Online publication date: 27-Jan-2016
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Koch PBagheri SKonen WFoussette CKrause PBäck TEsparcia-Alcázar A(2015)A New Repair Method For Constrained OptimizationProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754658(273-280)Online publication date: 11-Jul-2015
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The cardinality matrix constraint

Solving dynamic constraint optimization problems using ICHEA

Solving constrained optimization problems by using covariance matrix adaptation evolutionary strategy with constraint handling methods

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