Article

The micro genetic algorithm 2: towards online adaptation in evolutionary multiobjective optimization

Authors: Gregorio Toscano Pulido, Carlos A. Coello CoelloAuthors Info & Claims

EMO'03: Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization

Pages 252 - 266

Published: 08 April 2003 Publication History

Abstract

In this paper, we deal with an important issue generally omitted in the current literature on evolutionary multi-objective optimization: on-line adaptation. We propose a revised version of our micro-GA for multi-objective optimization which does not require any parameter fine-tuning. Furthermore, we introduce in this paper a dynamic selection scheme through which our algorithm decides which is the "best" crossover operator to be used at any given time. Such a scheme has helped to improve the performance of the new version of the algorithm which is called the micro-GA2 (µGA²). The new approach is validated using several test function and metrics taken from the specialized literature and it is compared to the NSGA-II and PAES.

References

[1]

Hussein A. Abbass. The Self-Adaptive Pareto Differential Evolution Algorithm. In Congress on Evolutionary Computation (CEC'2002), volume 1, pages 831-836, Piscataway, New Jersey, May 2002. IEEE Service Center.

[2]

Dirk Büche, Gianfranco Guidati, Peter Stoll, and Petros Kourmoursakos. Self-Organizing Maps for Pareto Optimization of Airfoils. In Juan Julián Merelo Guervós et al., editor, Parallel Problem Solving from Nature--PPSN VII, pages 122-131, Granada, Spain, September 2002. Springer-Verlag. Lecture Notes in Computer Science No. 2439.

Digital Library

[3]

Carlos A. Coello Coello and Gregorio Toscano Pulido. Multi-objective Optimization using a Micro-Genetic Algorithm. In Lee Spector et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2001), pages 274-282, San Francisco, California, 2001. Morgan Kaufmann Publishers.

[4]

Carlos A. Coello Coello, David A. Van Veldhuizen, and Gary B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, May 2002. ISBN 0-3064-6762-3.

Digital Library

[5]

Kalyanmoy Deb. Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 7(3):205-230, Fall 1999.

Digital Library

[6]

Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197, April 2002.

Digital Library

[7]

Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler. Scalable Multi-Objective Optimization Test Problems. In Congress on Evolutionary Computation (CEC'2002), volume 1, pages 825-830, Piscataway, New Jersey, May 2002. IEEE Service Center.

[8]

Joshua D. Knowles and David W. Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149-172, 2000.

Digital Library

[9]

Teuvo Kohonen, T.S. Huang, and M.R. Schroeder, editors. Self-Organizing Maps. Springer-Verlag, 2001.

Digital Library

[10]

Frank Kursawe. AVariant of Evolution Strategies for Vector Optimization. In H. P. Schwefel and R.Männer, editors, Parallel Problem Solving from Nature. 1st Workshop, PPSN I, volume 496 of Lecture Notes in Computer Science, pages 193-197, Berlin, Germany, Oct 1991. Springer-Verlag.

Digital Library

[11]

Marco Laumanns, Günter Rudolph, and Hans-Paul Schwefel. Mutation Control and Convergence in Evolutionary Multi-Objective Optimization. In Proceedings of the 7th International Mendel Conference on Soft Computing (MENDEL 2001), Brno, Czech Republic, June 2001.

[12]

Jason R. Schott. Fault Tolerant Design Using Single and Multi-criteria Genetic Algorithm Optimization. Master's thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, May 1995.

[13]

K.C. Tan, T.H. Lee, and E.F. Khor. Evolutionary Algorithms with Dynamic Population Size and Local Exploration for Multi-objective Optimization. IEEE Transactions on Evolutionary Computation, 5(6):565-588, December 2001.

Digital Library

[14]

David A. VanVeldhuizen. Multi-objective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio, May 1999.

[15]

David A. Van Veldhuizen and Gary B. Lamont. Multi-objective Evolutionary Algorithm Research: A History and Analysis. Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, 1998.

[16]

Zhong-Yao Zhu and Kwong-Sak Leung. Asynchronous Self-Adjustable Island Genetic Algorithm for Multi-Objective Optimization Problems. In Congress on Evolutionary Computation (CEC'2002), volume 1, pages 837-842, Piscataway, New Jersey, May 2002. IEEE Service Center.

Cited By

Tan CLim CCheah Y(2013)A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization ProblemsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.5555/2596166.259617324:3(483-495)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.5555/2596166.2596173
Liao ZMao XHannam PZhao T(2012)Adaptation methodology of CBR for environmental emergency preparedness system based on an Improved Genetic AlgorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.01.04439:8(7029-7040)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1016/j.eswa.2012.01.044
Valera García JGómez Garay VIrigoyen Gordo EArtaza Fano FLarrea Sukia M(2012)Intelligent Multi-Objective Nonlinear Model Predictive Control (iMO-NMPC)Expert Systems with Applications: An International Journal10.1016/j.eswa.2011.12.05239:7(6527-6540)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.12.052
Show More Cited By

Index Terms

The micro genetic algorithm 2: towards online adaptation in evolutionary multiobjective optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

Index terms have been assigned to the content through auto-classification.

Recommendations

A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
Recent Advances in Soft Computing: Theories and Applications

In this paper, a Modified micro Genetic Algorithm MmGA is proposed for undertaking Multi-objective Optimization Problems MOPs. An NSGA-II inspired elitism strategy and a population initialization strategy are embedded into the traditional micro Genetic ...
Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA)

A hybrid micro genetic algorithm (HMGA) is proposed for Pareto optimum search focusing on the Kursawe and ZDT test functions. HMGA is a fusion of the micro genetic algorithm (MGA) and the elitism concept of fast Pareto genetic algorithm. The ...
Multiobjective optimization using a micro-genetic algorithm
GECCO'01: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation

In this paper, we propose a micro genetic algorithm with three forms of elitism for multiobjective optimization. We show how this relatively simple algorithm coupled with an external file and a diversity approach based on geographical distribution can ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

EMO'03: Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization

April 2003

811 pages

ISBN:3540018697

Editors:
Carlos M. Fonseca
University of the Algarve, Faculty of Sciences and Technology, Centre for Intelligent Systems, Faro, Portugal
,
Peter J. Fleming
University of Sheffield, Department of Automatic Control and Systems Engineering, Sheffield, UK
,
Eckart Zitzler
Swiss Federal Institute of Technology, Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Zürich, Switzerland
,
Lothar Thiele
Swiss Federal Institute of Technology, Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Zürich, Switzerland
,
Kalyanmoy Deb
Indian Institute of Technology, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Kanpur, India

Sponsors

Fundação Luso-Americana para o Desenvolvimento
Fundação Calouste Gulbenkian
Fundação para a Ciência e a Tecnologia
Fundação Oriente
Universidade do Algarve

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 April 2003

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tan CLim CCheah Y(2013)A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization ProblemsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.5555/2596166.259617324:3(483-495)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.5555/2596166.2596173
Liao ZMao XHannam PZhao T(2012)Adaptation methodology of CBR for environmental emergency preparedness system based on an Improved Genetic AlgorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.01.04439:8(7029-7040)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1016/j.eswa.2012.01.044
Valera García JGómez Garay VIrigoyen Gordo EArtaza Fano FLarrea Sukia M(2012)Intelligent Multi-Objective Nonlinear Model Predictive Control (iMO-NMPC)Expert Systems with Applications: An International Journal10.1016/j.eswa.2011.12.05239:7(6527-6540)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.12.052
Coello C(2011)Evolutionary multi-objective optimizationProceedings of the Third Mexican conference on Pattern recognition10.5555/2026143.2026147(22-33)Online publication date: 29-Jun-2011
https://dl.acm.org/doi/10.5555/2026143.2026147
Datta DFigueira J(2011)Graph partitioning by multi-objective real-valued metaheuristicsApplied Soft Computing10.1016/j.asoc.2011.01.04411:5(3976-3987)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1016/j.asoc.2011.01.044
Cheong CTan KVeeravalli B(2009)A multi-objective evolutionary algorithm for examination timetablingJournal of Scheduling10.1007/s10951-008-0085-512:2(121-146)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.1007/s10951-008-0085-5
Hippolyte JBloch CChatonnay PEspanet CChamagne DLipson H(2007)A self-adaptive multiagent evolutionary algorithm for electrical machine designProceedings of the 9th annual conference on Genetic and evolutionary computation10.1145/1276958.1277198(1250-1255)Online publication date: 7-Jul-2007
https://dl.acm.org/doi/10.1145/1276958.1277198
Salazar DCarrasquero NGalván B(2005)Exploiting comparative studies using criteriaProceedings of the Third international conference on Evolutionary Multi-Criterion Optimization10.1007/978-3-540-31880-4_16(221-234)Online publication date: 9-Mar-2005
https://dl.acm.org/doi/10.1007/978-3-540-31880-4_16

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents