Abstract
Estimation of Distribution Algorithms (EDAs) use a probabilistic model of promising solutions found so far to obtain new candidate solutions of an optimization problem. This paper focuses on the design of parallel EDAs. More specifically, the paper describes a method for parallel construction of Bayesian networks with local structures in form of decision trees in the Mixed Bayesian Optimization Algorithm. The proposed Multithreaded Mixed Bayesian Optimization Algorithm (MMBOA) is intended for implementation on a cluster of workstations that communicate by Message Passing Interface (MPI). Communication latencies between workstations are eliminated by multithreaded processing, so in each workstation the high-priority model-building thread, which is communication demanding, can be overlapped by low-priority model sampling thread when necessary. High performance of MMBOA is verified via simulation in TRANSIM tool.
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References
Larrañaga, P.: A Review on Estimation of Distribution Algorithms. Estimation of Distribution Algorithms. A new Tool for Evolutionary Computation. P. Larrañaga, J.A. Lozano (eds.). Kluwer Academic Publishers, pp. 57–100, 2001.
Pelikan, M., Goldberg, D.E., Lobo, F.: A survey of optimization by building and using probabilistic models, IlliGAL Report No. 99018, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, Illinois, 1999.
Bosman, P.A.N., Thierens, D.: An algorithmic framework for density estimation based evolutionary algorithms. Utrecht University Technical Report UU-CS-1999-46, Utrecht, 1999.
Ocenasek, J., Schwarz, J.: The Parallel Bayesian Optimization Algorithm, In: Proceedings of the European Symposium on Computational Inteligence, Physica-Verlag, Kosice, Slovak Republic, pp. 61–67, 2000.
Ocenasek, J., Schwarz, J.: The Distributed Bayesian Optimization Algorithm for combinatorial optimization, EUROGEN 2001 — Evolutionary Methods for Design, Optimisation and Control, Athens, Greece, CIMNE, pp. 115–120, 2001.
Lozano, J. A., Sagarna, R., Larrañaga, P.: Parallel Estimation of Distribution Algorithms. Estimation of Distribution Algorithms. A new Tool for Evolutionary Computation. P. Larrañaga, J.A. Lozano (eds.). Kluwer Academic Publishers, pp. 129–145, 2001.
Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: The Bayesian optimization algorithm. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, vol. I, Orlando, FL, Morgan Kaufmann Publishers, pp. 525–532, 1999.
Etxeberria, R., Larrañaga, P.: Global optimization using Bayesian networks. Second Symposium on Artificial Intelligence (CIMAF-99), Habana, Cuba, pp 332–339, 1999.
Mühlenbein, H., Mahnig, T.: FDA-a scalable evolutionary algorithm for the optimization of additively decomposed functions. Evolutionary Computation, 7(4), pp. 353–376, 1999.
Pelikan, M., Goldberg, D.E., Sastry, K.: Bayesian Optimization Algorithm, Decision Graphs, and Occam’s Razor, IlliGAL Report No. 2000020, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 2000.
Ocenasek, J., Schwarz, J.: Estimation of Distribution Algorithm for mixed continuous discrete optimization problems, In: 2nd Euro-International Symposium on Computational Intelligence, Kosice, Slovakia, IOS Press, pp. 227–232, 2002.
Laumanns, M., Ocenasek, J.: Bayesian Optimization Algorithms for multi-objective optimization, In: Parallel Problem Solving from Nature — PPSN VII, Springer-Verlag, pp. 298–307, 2002.
Arst. R., Minsker, B.S., Goldberg, D.E.: Comparing Advanced Genetic Algorithms and Simple Genetic Algorithms for Groundwater Management. 2002 Water Resources Planning & Management Conference, Roanoke, VA, 2002.
Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Boston, MA: Kluwer Academic Publishers. 2000.
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Ocenasek, J., Schwarz, J., Pelikan, M. (2003). Design of Multithreaded Estimation of Distribution Algorithms. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_1
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DOI: https://doi.org/10.1007/3-540-45110-2_1
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