MIGRATING INDIVIDUALS AND PROBABILISTIC MODELS ON DEDAS: A COMPARISON ON CONTINUOUS FUNCTIONS

One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in the Estimation of Distribution Algorithms (EDAs). EDAs constitute a well-known family of Evolutionary Computation techniques, similar to Genetic Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve EDAs from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called island-based models. This approach defines several islands (EDA instances) running independently and exchanging information with a given frequency. The information sent by the islands can be a set of individuals or a probabilistic model. This paper presents a comparative study of both information exchanging techniques for a univariate EDA ( UMDA g) over a wide set of parameters and problems –the standard benchmark developed for the IEEE Workshop on Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems of the ISDA 2009 Conference. The study concludes that the configurations based on migrating individuals obtain better results.

[1]  Enrique Alba,et al.  Parallel evolutionary algorithms can achieve super-linear performance , 2002, Inf. Process. Lett..

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

[3]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[4]  Pedro Larrañaga,et al.  Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.

[5]  Jose Miguel Puerta,et al.  Migration of Probability Models Instead of Individuals: An Alternative When Applying the Island Model to EDAs , 2004, PPSN.

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

[7]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[8]  Alberto Ochoa,et al.  A Parallel Island Model for Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.

[9]  Jirí Jaros,et al.  Parallel BMDA with an aggregation of probability models , 2009, 2009 IEEE Congress on Evolutionary Computation.

[10]  Ponnuthurai Nagaratnam Suganthan,et al.  Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization , 2008 .

[11]  Martin Pelikan,et al.  Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) , 2006 .

[12]  Enrique Alba,et al.  Improving flexibility and efficiency by adding parallelism to genetic algorithms , 2002, Stat. Comput..

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

[14]  Chrisila C. Pettey,et al.  A Theoretical Investigation of a Parallel Genetic Algorithm , 1989, ICGA.

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

[16]  A. A. Zhigli︠a︡vskiĭ,et al.  Theory of Global Random Search , 1991 .

[17]  Víctor Robles,et al.  Machine Learning to Analyze Migration Parameters in Parallel Genetic Algorithms , 2008, Innovations in Hybrid Intelligent Systems.

[18]  Jirí Jaros,et al.  Parallel BMDA with probability model migration , 2007, 2007 IEEE Congress on Evolutionary Computation.

[19]  Dana S. Richards,et al.  Punctuated Equilibria: A Parallel Genetic Algorithm , 1987, ICGA.

[20]  J. A. Lozano,et al.  Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing) , 2006 .

[21]  David E. Goldberg,et al.  Multiple-Deme Parallel Estimation of Distribution Algorithms: Basic Framework and Application , 2003, PPAM.

[22]  Jose Miguel Puerta,et al.  Initial approaches to the application of islands-based parallel EDAs in continuous domains , 2005, 2005 International Conference on Parallel Processing Workshops (ICPPW'05).