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Parameter-less evolutionary search

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Gregor PapaAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1133 - 1134

https://doi.org/10.1145/1389095.1389314

Published: 12 July 2008 Publication History

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Abstract

The paper presents the parameter-less implementation of an evolutionary-based search. It does not need any predefined control parameters values, which are usually used for genetic algorithms and similar techniques. Efficiency of the proposed algorithm was evaluated by CEC2006 benchmark functions and a real-world product optimization problem.

References

[1]

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G. R. Harik and F. G. Lobo. A parameter-less genetic algorithm. In GECCO 1999, pages 258--265, July 1999.

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J. Liang, T. Runarsson, E. Mezura-Montes, M. Clerc, P. Suganthan,C. C. Coello, and K. Deb. Problem definitions and evaluation criteria for the cec 2006 special session on constrained real-parameter optimization. Technical Report 2006005, Nanyang Technological University, Singapore, March 2006.

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G. Papa. Concurrent operation scheduling and unit allocation with an evolutionary technique in the process of integrated-circuit design. PhD thesis, University of Ljubljana, Ljubljana, Slovenia, October 2002.

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T. Tušar, P. Korošec, G. Papa, B. Filipič, and J. Šilc. A comparative study of stochastic optimization methods in electric motor design. Applied Intelligence, 27(2):101--111, October 2007.

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Cited By

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Papa GKrawiec K(2021)Applications of dynamic parameter control in evolutionary computationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461435(1064-1088)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3461435
Papa GDoerr CCoello Coello C(2020)Dynamic control parameter choices in evolutionary computationProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3389876(927-956)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3389876
Rao RRao R(2015)Applications of TLBO Algorithm and Its Modifications to Different Engineering and Science DisciplinesTeaching Learning Based Optimization Algorithm10.1007/978-3-319-22732-0_15(223-267)Online publication date: 15-Nov-2015
https://doi.org/10.1007/978-3-319-22732-0_15
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Index Terms

Parameter-less evolutionary search
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

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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

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Papa GKrawiec K(2021)Applications of dynamic parameter control in evolutionary computationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461435(1064-1088)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3461435
Papa GDoerr CCoello Coello C(2020)Dynamic control parameter choices in evolutionary computationProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3389876(927-956)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3389876
Rao RRao R(2015)Applications of TLBO Algorithm and Its Modifications to Different Engineering and Science DisciplinesTeaching Learning Based Optimization Algorithm10.1007/978-3-319-22732-0_15(223-267)Online publication date: 15-Nov-2015
https://doi.org/10.1007/978-3-319-22732-0_15
Črepinšek MLiu SMernik LMernik M(2014)Is a comparison of results meaningful from the inexact replications of computational experiments?Soft Computing10.1007/s00500-014-1493-420:1(223-235)Online publication date: 1-Nov-2014
https://doi.org/10.1007/s00500-014-1493-4
Korošec PPapa G(2013)Metaheuristic approach to transportation scheduling in emergency situationsTransport10.3846/16484142.2013.78154028:1(46-59)Online publication date: 3-Apr-2013
https://doi.org/10.3846/16484142.2013.781540
Kamrath NGoldman BTauritz DAlba E(2013)Using supportive coevolution to evolve self-configuring crossoverProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2482727(1489-1496)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2482727
Papa GSilc J(2013)The Parameter-less Evolutionary Search for real-parameter single objective optimization2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557693(1131-1137)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557693
KorošEc PBole UPapa G(2013)A multi-objective approach to the application of real-world production schedulingExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.05.03540:15(5839-5853)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1016/j.eswa.2013.05.035
Papa G(2013)Parameter-less algorithm for evolutionary-based optimizationComputational Optimization and Applications10.1007/s10589-013-9565-456:1(209-229)Online publication date: 1-Sep-2013
https://dl.acm.org/doi/10.1007/s10589-013-9565-4
Scheibenpflug AWagner SPitzer EAffenzeller MMoore J(2012)Optimization knowledge baseProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330806(141-148)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330784.2330806
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Fast and efficient black box optimization using the parameter-less population pyramid

Parameter-less algorithm for evolutionary-based optimization

Parameter-less population pyramid

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