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Credit assignment in adaptive evolutionary algorithms

Published: 08 July 2006 Publication History

Abstract

In this paper, a new method for assigning credit to search operators is presented. Starting with the principle of optimizing search bias, search operators are selected based on an ability to create solutions that are historically linked to future generations. Using a novel framework for defining performance measurements, distributing credit for performance, and the statistical interpretation of this credit, a new adaptive method is developed and shown to outperform a variety of adaptive and non-adaptive competitors.

References

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Barbosa, H. J. C. and e Sá, A. M. On Adaptive Operator Probabilities in Real Coded Genetic Algorithms, In Workshop on Advances and Trends in Artificial Intelligence for Problem Solving (SCCC '00), (Santiago, Chile, November 2000).
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Davis, L. Handbook of Genetic Algorithms, van Nostrand Reinhold, New York, 1991.
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De Jong, K. An analysis of the behaviour of a class of genetic adaptive systems. Ph. D Thesis, University of Michigan, Ann Arbor, Michigan, 1975.
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Herrera, F., Lozano, M., and Sánchez, A. M. 2005. Hybrid crossover operators for real-coded genetic algorithms: an experimental study. Soft Comput. 9, 4 (Apr. 2005), 280--298.
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Janka, E. Vergleich stochastischer Verfahren zur globalen Optimierung, Diploma Thesis, University of Vienna, Vienna, Austria, 1999.
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Julstrom, B. A. Adaptive operator probabilities in a genetic algorithm that applies three operators. In Proceedings of the 1997 ACM Symposium on Applied Computing (SAC '97) (San Jose, California, United States). ACM Press, New York, NY, 233--238, 1997.
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Muhlenbein, H., Schomisch, M. and Born, J. The parallel genetic algorithm as function optimizer. In Proc. of 4th International Conference of Genetic Algorithms, 271--278, 1991.
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Pham, Q.T. Dynamic Optimization of Chemical Engineering Processes by an Evolutionary Method. Comp. Chem. Eng., 22 (1998), 1089--1097.
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Pham, Q. T. Competitive evolution: a natural approach to operator selection. In: Progress in Evolutionary Computation, Lecture Notes in Artificial Intelligence, (Evolutionary Computation Workshop) (Armidale, Australia, November 21-22, 1994). Springer-Verlag, Heidelberg, 1995, 49--60.
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Storn, R. and Price, K. Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA, 1995.
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Whitacre, J., Pham, Q.T., Sarker, R. Use of Statistical Outlier Detection Method in Adaptive Evolutionary Algorithms. In Proceedings of the 2006 Conference on Genetic and Evolutionary Computation (GECCO '05) (Seattle, USA, July 8-12, 2006). ACM Press, New York, NY, 2006.

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  • (2014)An ensemble algorithm with self-adaptive learning techniques for high-dimensional numerical optimizationApplied Mathematics and Computation10.5555/2942969.2943068231:C(329-346)Online publication date: 15-Mar-2014
  • (2014)Cooperative optimization for efficient financial time series forecasting2014 International Conference on Computing for Sustainable Global Development (INDIACom)10.1109/IndiaCom.2014.6828114(124-129)Online publication date: Mar-2014
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Published In

cover image ACM Conferences
GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation
July 2006
2004 pages
ISBN:1595931864
DOI:10.1145/1143997
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: 08 July 2006

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Author Tags

  1. adaptation
  2. evolutionary algorithm
  3. genetic algorithm
  4. historical credit assignment
  5. search bias

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GECCO06
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GECCO06: Genetic and Evolutionary Computation Conference
July 8 - 12, 2006
Washington, Seattle, USA

Acceptance Rates

GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2018)Drone Squadron OptimizationNeural Computing and Applications10.1007/s00521-017-2881-330:10(3117-3144)Online publication date: 1-Nov-2018
  • (2014)An ensemble algorithm with self-adaptive learning techniques for high-dimensional numerical optimizationApplied Mathematics and Computation10.5555/2942969.2943068231:C(329-346)Online publication date: 15-Mar-2014
  • (2014)Cooperative optimization for efficient financial time series forecasting2014 International Conference on Computing for Sustainable Global Development (INDIACom)10.1109/IndiaCom.2014.6828114(124-129)Online publication date: Mar-2014
  • (2014)Self-adaptive Systems: Facilitating the Use of Combinatorial Problem SolversHCI International 2014 - Posters’ Extended Abstracts10.1007/978-3-319-07857-1_88(503-508)Online publication date: 2014
  • (2012)Estimating meme fitness in adaptive memetic algorithms for combinatorial problemsEvolutionary Computation10.1162/EVCO_a_0006020:2(165-188)Online publication date: 1-Jun-2012
  • (2011)A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability testSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-010-0646-315:11(2187-2199)Online publication date: 1-Nov-2011
  • (2010)Population-based algorithm portfolios for numerical optimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2010.204018314:5(782-800)Online publication date: 1-Oct-2010
  • (2010)What Is Autonomous Search?Hybrid Optimization10.1007/978-1-4419-1644-0_11(357-391)Online publication date: 23-Oct-2010
  • (2009)Making and breaking power laws in evolutionary algorithm population dynamicsMemetic Computing10.1007/s12293-009-0009-81:2(125-137)Online publication date: 1-Apr-2009

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