GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm

Hybrid metaheuristics have received considerable interest in recent years. A wide variety of hybrid approaches have been proposed in the literature. In this paper a new hybrid approach, named GA-EDA, is presented. This new hybrid algorithm is based on genetic and estimation of distribution algorithms. The original objective is to benefit from both approaches and attempt to achieve improved results in exploring the search space. In order to perform an evaluation of this new approach, a selection of synthetic optimization problems have been proposed, together with some real-world cases. Experimental results show the competitiveness of our new approach.

[1]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[2]  W. D. Harvey,et al.  Nonsystematic backtracking search , 1995 .

[3]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[4]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[5]  Pedro Larrañaga,et al.  Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks , 2000 .

[6]  Teodor Gabriel Crainic,et al.  Systemic Behavior of Cooperative Search Algorithms , 2002, Parallel Comput..

[7]  Pedro Larrañaga,et al.  GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms , 2004 .

[8]  D. Hand,et al.  Idiot's Bayes—Not So Stupid After All? , 2001 .

[9]  El-Ghazali Talbi,et al.  A Taxonomy of Hybrid Metaheuristics , 2002, J. Heuristics.

[10]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[11]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[12]  José Torres-Jiménez,et al.  ERA: An Algorithm for Reducing the Epistasis of SAT Problems , 2003, GECCO.

[13]  Tad Hogg,et al.  Solving the Really Hard Problems with Cooperative Search , 1993, AAAI.

[14]  Francisco Herrera,et al.  Gradual distributed real-coded genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[15]  El-Ghazali Talbi,et al.  COSEARCH: a co-evolutionary metaheuristic , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[16]  Thomas Bäck,et al.  Intelligent Mutation Rate Control in Canonical Genetic Algorithms , 1996, ISMIS.

[17]  L. Darrell Whitley,et al.  GENITOR II: a distributed genetic algorithm , 1990, J. Exp. Theor. Artif. Intell..

[18]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[19]  Andrea Lodi,et al.  Local Search and Constraint Programming , 2003, Handbook of Metaheuristics.

[20]  F. H. Branin Widely convergent method for finding multiple solutions of simultaneous nonlinear equations , 1972 .

[21]  Stefan Voß,et al.  Cooperative Intelligent Search Using Adaptive Memory Techniques , 1999 .

[22]  Theodore C. Belding,et al.  The Distributed Genetic Algorithm Revisited , 1995, ICGA.

[23]  Aimo A. Törn,et al.  Stochastic Global Optimization: Problem Classes and Solution Techniques , 1999, J. Glob. Optim..

[24]  Bart Selman,et al.  Systematic Versus Stochastic Constraint Satisfaction , 1995, IJCAI.

[25]  Qingfu Zhang,et al.  Combination of Guided Local Search and Estimation of Distribution Algorithm for Quadratic Assignment Problems , 2006 .

[26]  Melanie Mitchell,et al.  The royal road for genetic algorithms: Fitness landscapes and GA performance , 1991 .

[27]  David Mark Levine,et al.  A parallel genetic algorithm for the set partitioning problem , 1995 .

[28]  David E. Goldberg,et al.  Parallel Recombinative Simulated Annealing: A Genetic Algorithm , 1995, Parallel Comput..

[29]  Heinz Mühlenbein,et al.  The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

[30]  Charles E. Taylor Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Complex Adaptive Systems.John H. Holland , 1994 .

[31]  Pedro Larrañaga,et al.  Feature Subset Selection by Bayesian network-based optimization , 2000, Artif. Intell..

[32]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[33]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[34]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[35]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[36]  Kwan Hua. Sim Incorporating genetic algorithm into simulated annealing based redistricting , 2002 .

[37]  María S. Pérez-Hernández,et al.  Parallel Stochastic Search for Protein Secondary Structure Prediction , 2003, PPAM.

[38]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[39]  Erick Cantú-Paz,et al.  Efficient and Accurate Parallel Genetic Algorithms , 2000, Genetic Algorithms and Evolutionary Computation.

[40]  Fred W. Glover,et al.  Tabu Search , 1997, Handbook of Heuristics.

[41]  Jin-Kao Hao,et al.  A Hybrid Genetic Algorithm for the Satisfiability Problem , 2002 .

[42]  Fred W. Glover,et al.  Multi-level Cooperative Search: A New Paradigm for Combinatorial Optimization and an Application to Graph Partitioning , 1999, Euro-Par.

[43]  D. Thierens Adaptive mutation rate control schemes in genetic algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[44]  Olivier C. Martin,et al.  Combining simulated annealing with local search heuristics , 1993, Ann. Oper. Res..

[45]  Jörg Denzinger,et al.  On cooperation between evolutionary algorithms and other search paradigms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).