Efficient parallel genetic algorithms: theory and practice
Parallel genetic algorithms (GAs) are complex programs that are controlled by many parameters, which affect their search quality and their efficiency. The goal of this paper is to provide guidelines to choose those parameters rationally. The investigation centers on the sizing of populations, becaus...
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| Published in: | Computer methods in applied mechanics and engineering Vol. 186; no. 2; pp. 221 - 238 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.01.2000
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| ISSN: | 0045-7825, 1879-2138 |
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| Abstract | Parallel genetic algorithms (GAs) are complex programs that are controlled by many parameters, which affect their search quality and their efficiency. The goal of this paper is to provide guidelines to choose those parameters rationally. The investigation centers on the sizing of populations, because previous studies show that there is a crucial relation between solution quality and population size. As a first step, the paper shows how to size a simple GA to reach a solution of a desired quality. The simple GA is then parallelized, and its execution time is optimized. The rest of the paper deals with parallel GAs with multiple populations. Two bounding cases of the migration rate and topology are analyzed, and the case that yields good speedups is optimized. Later, the models are specialized to consider sparse topologies and migration rates that are more likely to be used by practitioners. The paper also presents the additional advantages of combining multi- and single-population parallel GAs. The results of this work are simple models that practitioners may use to design efficient and competent parallel GAs. |
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| AbstractList | Parallel genetic algorithms (GAs) are complex programs that are controlled by many parameters, which affect their search quality and their efficiency. The goal of this paper is to provide guidelines to choose those parameters rationally. The investigation centers on the sizing of populations, because previous studies show that there is a crucial relation between solution quality and population size. As a first step, the paper shows how to size a simple GA to reach a solution of a desired quality. The simple GA is then parallelized, and its execution time is optimized. The rest of the paper deals with parallel GAs with multiple populations. Two bounding cases of the migration rate and topology are analyzed, and the case that yields good speedups is optimized. Later, the models are specialized to consider sparse topologies and migration rates that are more likely to be used by practitioners. The paper also presents the additional advantages of combining multi- and single-population parallel GAs. The results of this work are simple models that practitioners may use to design efficient and competent parallel GAs. |
| Author | Cantú-Paz, Erick Goldberg, David E. |
| Author_xml | – sequence: 1 givenname: Erick surname: Cantú-Paz fullname: Cantú-Paz, Erick email: cantupaz@illigal.ge.uiuc.edu organization: Department of Computer Science and Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana–Champaign, 117 Transportation Bldg., 104 South Matthews Ave., Urbana, USA – sequence: 2 givenname: David E. surname: Goldberg fullname: Goldberg, David E. email: deg@illigal.ge.uiuc.edu organization: Department of General Engineering and Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana–Champaign, Urbana, USA |
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| References | Fogarty, Huang (BIB12) 1991 R. Hauser, R. Männer, Implementation of standard genetic algorithm on MIMD machines, in: Y. Davidor, H.-P. Schwefel, R. Männer (Eds.), Parallel Problem Solving from Nature, PPSN III, Springer, Berlin, 1994, pp. 504–513 M. Abramovitz, I. Stegun (Eds.), Handbook of Mathematic Functions with Formulas, Graphs, and Mathematical Tables, Dover Publications, New York, 1972 Deb, Goldberg (BIB10) 1993 E. Cantú-Paz, Using Markov chains to analyze a bounding case of parallel genetic algorithms, in: J.R. Koza, W. Banzhaf, K. Chellapilla, M. Deb, K. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, R.L. Riolo (Eds.), Genetic Programming 1998: Proceedings of the Third Annual Conference, Morgan Kaufmann, San Francisco, CA, 1998, pp. 456–462 P.B. Grosso, Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model, Ph.D. Thesis, The University of Michigan, 1985 Goldberg, Deb, Clark (BIB14) 1992; 6 Bianchini, Brown (BIB4) 1993; 6 Goldberg (BIB13) 1989 G. Harik, E. Cantú-Paz, D.E. Goldberg, B.L. Miller, The gambler's ruin problem, genetic algorithms, and the sizing of populations, in: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, IEEE, Piscataway, NJ, 1997, pp. 7–12 E. Cantú-Paz, D.E. Goldberg, Modeling idealized bounding cases of parallel genetic algorithms, in: J. Koza, K. Deb, M. Dorigo, D. Fogel, M. Garzon, H. Iba, R. Riolo (Eds.), Genetic Programming 1997: Proceedings of the Second Annual Conference, Morgan Kaufmann, San Francisco, CA, 1997, pp. 353–361 Leon Harter (BIB18) 1970 D. Abramson, G. Mills, S. Perkins, Parallelisation of a genetic algorithm for the computation of efficient train schedules, in: Proceedings of the 1993 Parallel Computing and Transputers Conference, 1993, pp. 139–149 M. Munetomo, Y. Takai, Y. Sato, An efficient migration scheme for subpopulation-based asynchronously parallel genetic algorithms, in: S. Forrest (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1993, p. 649 M. Valenzuela-Rendón, Two analysis tools to describe the operation of classifier systems, Ph.D. Thesis, University of Alabama, Tuscaloosa, 1989 E. Cantú-Paz, D.E. Goldberg, Predicting speedups of idealized bounding cases of parallel genetic algorithms, in: T. Bäck (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco, 1997, pp. 113–121 J.J. Grefenstette, Parallel adaptive algorithms for function optimization, Tech. Rep. No. CS-81-19, Vanderbilt University, Computer Science Department, Nashville, TN, 1981 Braun (BIB5) 1990 W. Feller, An Introduction to Probability Theory and its Applications, second ed., vol. 1, Wiley, New York, 1966 Cantú-Paz (BIB6) 1998; 10 A.D. Bethke, Comparison of genetic algorithms and gradient-based optimizers on parallel processors: efficiency of use of processing capacity, Tech. Rep. No. 197, University of Michigan, Logic of Computers Group, Ann Arbor, MI, 1976 D. Thierens, D.E. Goldberg, Mixing in genetic algorithms, in: S. Forrest (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1993, pp. 38–45 Goldberg (10.1016/S0045-7825(99)00385-0_BIB13) 1989 10.1016/S0045-7825(99)00385-0_BIB19 10.1016/S0045-7825(99)00385-0_BIB15 10.1016/S0045-7825(99)00385-0_BIB16 10.1016/S0045-7825(99)00385-0_BIB17 10.1016/S0045-7825(99)00385-0_BIB2 10.1016/S0045-7825(99)00385-0_BIB21 10.1016/S0045-7825(99)00385-0_BIB1 10.1016/S0045-7825(99)00385-0_BIB11 10.1016/S0045-7825(99)00385-0_BIB22 10.1016/S0045-7825(99)00385-0_BIB3 Bianchini (10.1016/S0045-7825(99)00385-0_BIB4) 1993; 6 Cantú-Paz (10.1016/S0045-7825(99)00385-0_BIB6) 1998; 10 Braun (10.1016/S0045-7825(99)00385-0_BIB5) 1990 Goldberg (10.1016/S0045-7825(99)00385-0_BIB14) 1992; 6 Leon Harter (10.1016/S0045-7825(99)00385-0_BIB18) 1970 10.1016/S0045-7825(99)00385-0_BIB20 10.1016/S0045-7825(99)00385-0_BIB9 Fogarty (10.1016/S0045-7825(99)00385-0_BIB12) 1991 10.1016/S0045-7825(99)00385-0_BIB8 10.1016/S0045-7825(99)00385-0_BIB7 Deb (10.1016/S0045-7825(99)00385-0_BIB10) 1993 |
| References_xml | – reference: P.B. Grosso, Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model, Ph.D. Thesis, The University of Michigan, 1985 – reference: D. Thierens, D.E. Goldberg, Mixing in genetic algorithms, in: S. Forrest (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1993, pp. 38–45 – reference: W. Feller, An Introduction to Probability Theory and its Applications, second ed., vol. 1, Wiley, New York, 1966 – reference: M. Valenzuela-Rendón, Two analysis tools to describe the operation of classifier systems, Ph.D. Thesis, University of Alabama, Tuscaloosa, 1989 – start-page: 145 year: 1991 end-page: 149 ident: BIB12 article-title: Implementing the genetic algorithm on transputer based parallel processing systems publication-title: Parallel Problem Solving from Nature – start-page: 129 year: 1990 end-page: 133 ident: BIB5 article-title: On solving travelling salesman problems by genetic algorithms publication-title: Parallel Problem Solving from Nature – reference: M. Munetomo, Y. Takai, Y. Sato, An efficient migration scheme for subpopulation-based asynchronously parallel genetic algorithms, in: S. Forrest (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1993, p. 649 – reference: M. Abramovitz, I. Stegun (Eds.), Handbook of Mathematic Functions with Formulas, Graphs, and Mathematical Tables, Dover Publications, New York, 1972 – volume: 10 start-page: 141 year: 1998 end-page: 171 ident: BIB6 article-title: A survey of parallel genetic algorithms publication-title: Calculateurs Parallèles Reseaux et Systems Repartis – reference: G. Harik, E. Cantú-Paz, D.E. Goldberg, B.L. Miller, The gambler's ruin problem, genetic algorithms, and the sizing of populations, in: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, IEEE, Piscataway, NJ, 1997, pp. 7–12 – reference: A.D. Bethke, Comparison of genetic algorithms and gradient-based optimizers on parallel processors: efficiency of use of processing capacity, Tech. Rep. No. 197, University of Michigan, Logic of Computers Group, Ann Arbor, MI, 1976 – year: 1989 ident: BIB13 publication-title: Genetic Algorithms in Search, Optimization, and Machine Learning – reference: E. Cantú-Paz, D.E. Goldberg, Modeling idealized bounding cases of parallel genetic algorithms, in: J. Koza, K. Deb, M. Dorigo, D. Fogel, M. Garzon, H. Iba, R. Riolo (Eds.), Genetic Programming 1997: Proceedings of the Second Annual Conference, Morgan Kaufmann, San Francisco, CA, 1997, pp. 353–361 – reference: E. Cantú-Paz, D.E. Goldberg, Predicting speedups of idealized bounding cases of parallel genetic algorithms, in: T. Bäck (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco, 1997, pp. 113–121 – reference: R. Hauser, R. Männer, Implementation of standard genetic algorithm on MIMD machines, in: Y. Davidor, H.-P. Schwefel, R. Männer (Eds.), Parallel Problem Solving from Nature, PPSN III, Springer, Berlin, 1994, pp. 504–513 – volume: 6 start-page: 333 year: 1992 end-page: 362 ident: BIB14 article-title: Genetic algorithms, noise, and the sizing of populations publication-title: Complex Systems – reference: E. Cantú-Paz, Using Markov chains to analyze a bounding case of parallel genetic algorithms, in: J.R. Koza, W. Banzhaf, K. Chellapilla, M. Deb, K. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, R.L. Riolo (Eds.), Genetic Programming 1998: Proceedings of the Third Annual Conference, Morgan Kaufmann, San Francisco, CA, 1998, pp. 456–462 – start-page: 93 year: 1993 end-page: 108 ident: BIB10 article-title: Analyzing deception in trap functions publication-title: Foundations of Genetic Algorithms 2 – year: 1970 ident: BIB18 publication-title: Order Statistics and their Use in Testing and Estimation – reference: J.J. Grefenstette, Parallel adaptive algorithms for function optimization, Tech. Rep. No. CS-81-19, Vanderbilt University, Computer Science Department, Nashville, TN, 1981 – reference: D. Abramson, G. Mills, S. Perkins, Parallelisation of a genetic algorithm for the computation of efficient train schedules, in: Proceedings of the 1993 Parallel Computing and Transputers Conference, 1993, pp. 139–149 – volume: 6 start-page: 67 year: 1993 end-page: 82 ident: BIB4 article-title: Parallel genetic algorithms on distributed-memory architectures publication-title: Transputer Research and Applications – ident: 10.1016/S0045-7825(99)00385-0_BIB1 – ident: 10.1016/S0045-7825(99)00385-0_BIB2 – volume: 10 start-page: 141 issue: 2 year: 1998 ident: 10.1016/S0045-7825(99)00385-0_BIB6 article-title: A survey of parallel genetic algorithms publication-title: Calculateurs Parallèles Reseaux et Systems Repartis – start-page: 93 year: 1993 ident: 10.1016/S0045-7825(99)00385-0_BIB10 article-title: Analyzing deception in trap functions doi: 10.1016/B978-0-08-094832-4.50012-X – ident: 10.1016/S0045-7825(99)00385-0_BIB11 – volume: 6 start-page: 67 year: 1993 ident: 10.1016/S0045-7825(99)00385-0_BIB4 article-title: Parallel genetic algorithms on distributed-memory architectures publication-title: Transputer Research and Applications – ident: 10.1016/S0045-7825(99)00385-0_BIB17 doi: 10.1109/ICEC.1997.592259 – ident: 10.1016/S0045-7825(99)00385-0_BIB19 doi: 10.1007/3-540-58484-6_293 – start-page: 145 year: 1991 ident: 10.1016/S0045-7825(99)00385-0_BIB12 article-title: Implementing the genetic algorithm on transputer based parallel processing systems publication-title: Parallel Problem Solving from Nature doi: 10.1007/BFb0029745 – ident: 10.1016/S0045-7825(99)00385-0_BIB16 – year: 1989 ident: 10.1016/S0045-7825(99)00385-0_BIB13 – ident: 10.1016/S0045-7825(99)00385-0_BIB20 – ident: 10.1016/S0045-7825(99)00385-0_BIB15 – ident: 10.1016/S0045-7825(99)00385-0_BIB22 – ident: 10.1016/S0045-7825(99)00385-0_BIB21 – volume: 6 start-page: 333 year: 1992 ident: 10.1016/S0045-7825(99)00385-0_BIB14 article-title: Genetic algorithms, noise, and the sizing of populations publication-title: Complex Systems – start-page: 129 year: 1990 ident: 10.1016/S0045-7825(99)00385-0_BIB5 article-title: On solving travelling salesman problems by genetic algorithms – ident: 10.1016/S0045-7825(99)00385-0_BIB3 – year: 1970 ident: 10.1016/S0045-7825(99)00385-0_BIB18 – ident: 10.1016/S0045-7825(99)00385-0_BIB7 – ident: 10.1016/S0045-7825(99)00385-0_BIB8 – ident: 10.1016/S0045-7825(99)00385-0_BIB9 |
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