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GARS: an improved genetic algorithm with reserve selection for global optimization

Published: 07 July 2007 Publication History

Abstract

This paper investigates how genetic algorithms (GAs) can be improved to solve large-scale and complex problems more efficiently. First of all, we review premature convergence, one of the challenges confronted with when applying GAs to real-world problems. Next, some of the methods now available to prevent premature convergence and their intrinsic defects are discussed. A qualitative analysis is then done on the cause of premature convergence that is the loss of building blocks hosted in less-fit individuals during the course of evolution. Thus, we propose a new improver - GAs with Reserve Selection (GARS), where a reserved area is set up to save potential building blocks and a selection mechanism based on individual uniqueness is employed to activate the potentials. Finally, case studies are done in a few standard problems well known in the literature, where the experimental results demonstrate the effectiveness and robustness of GARS in suppressing premature convergence, and also an enhancement is found in global optimization capacity.

References

[1]
J. Andre, P. Siarry, and T. Dognon. An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization. Advances in Engineering Software, 32:49--60, 2001.
[2]
H. G. Cobb and J. J. Grefenstette. Genetic algorithms for tracking changing environments. In S. Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 523--530, San Mateo, CA, 1993. Morgan Kaufmann.
[3]
L. Davis, editor. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991.
[4]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182--197, April 2002.
[5]
K. A. De Jong. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Doctoral thesis, Department of Computer and Communication Sciences, University of Michigan, Ann Arbor, 1975.
[6]
D. B. Fogel. An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks, 5(1):3--14, January 1994.
[7]
D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, 1989.
[8]
J. J. Grefenstette. Genetic algorithms for changing environments. In R. Manner and B. Manderick, editors, Parallel Problem Solving from Nature 2, pages 137--144, Amsterdam, North Holland, 1992.
[9]
J. H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, 1975.
[10]
J. Ma and et al. The great mutation used to improve the searching quality of GA. Control Theory and Applications, 15(3):404--407, 1998. In Chinese.
[11]
Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin Heidelberg New York, 1996.
[12]
NIAS DNA Bank SRS database. Website http://srs.dna.affrc.go.jp/srs8/.
[13]
G. Reinelt. TSPLIB -- a traveling salesman problem library. ORSA J. Comput., 3:376--384, 1991.
[14]
M. Srinivas and L. M. Patnaik. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 24(4):656--667, April 1994.
[15]
L. Wang and T. Jiang. On the complexity of multiple sequence alignment. Journal of Computational Biology, 1(4):337--348, 1994.

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  • (2016)A Cultivated Variant of Differential Evolution Algorithm for Global OptimizationProblem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications10.4018/978-1-4666-9885-7.ch001(1-19)Online publication date: 2016
  • (2015)A Cultivated Differential Evolution Variant for Molecular Potential Energy ProblemProcedia Computer Science10.1016/j.procs.2015.07.42957(1265-1272)Online publication date: 2015
  • (2012)Analysis of a triploid genetic algorithm over deceptive and epistatic landscapesACM SIGAPP Applied Computing Review10.1145/2387358.238736212:3(51-59)Online publication date: 1-Sep-2012
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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
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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Publication History

Published: 07 July 2007

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Author Tags

  1. building block hypothesis
  2. evolutionary computation
  3. genetic algorithms
  4. population diversity
  5. premature convergence
  6. reserve selection

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2016)A Cultivated Variant of Differential Evolution Algorithm for Global OptimizationProblem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications10.4018/978-1-4666-9885-7.ch001(1-19)Online publication date: 2016
  • (2015)A Cultivated Differential Evolution Variant for Molecular Potential Energy ProblemProcedia Computer Science10.1016/j.procs.2015.07.42957(1265-1272)Online publication date: 2015
  • (2012)Analysis of a triploid genetic algorithm over deceptive and epistatic landscapesACM SIGAPP Applied Computing Review10.1145/2387358.238736212:3(51-59)Online publication date: 1-Sep-2012
  • (2012)Analysis of a triploid genetic algorithm over deceptive landscapesProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2245324(244-249)Online publication date: 26-Mar-2012
  • (2011)An analysis of multi-chromosome GAs in deceptive problemsProceedings of the 13th annual conference on Genetic and evolutionary computation10.1145/2001576.2001715(1021-1028)Online publication date: 12-Jul-2011
  • (2008)Multiple Sequence Alignment Based on Genetic Algorithms with Reserve Selection2008 IEEE International Conference on Networking, Sensing and Control10.1109/ICNSC.2008.4525460(1511-1516)Online publication date: Apr-2008
  • (2008)Solving deceptive problems using a genetic algorithm with reserve selection2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)10.1109/CEC.2008.4630900(884-889)Online publication date: Jun-2008
  • (2007)Optimizing reserve size in genetic algorithms with reserve selection using reinforcement learningSICE Annual Conference 200710.1109/SICE.2007.4421191(1341-1347)Online publication date: Sep-2007
  • (2007)Performance tuning of genetic algorithms with reserve selection2007 IEEE Congress on Evolutionary Computation10.1109/CEC.2007.4424745(2202-2209)Online publication date: Sep-2007

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