A meta-parallel evolutionary system for solving optimization problems

The purpose of the Meta-Parallel Evolutionary System (MPES) is to develop fast, efficient parallel evolutionary systems for function optimization. Given an optimization problem and a set number of nodes available for the computation, the MPES searches for a strong, potentially heterogeneous combination of evolutionary algorithms to coordinate in order to effectively solve a problem. The Evolutionary Algorithms that are utilized in the parallel system are a Particle Swarm Optimizer (PSO), a variety of Genetic Algorithms (GAs), and an Evolutionary Hill-Climber Algorithm (EHC). The subpopulations communicate with each other via one or more centralized buffers. At a higher level exists the MPES, which uses evolutionary methods in order to discover parameters for effective parallel systems. This methodology provides an immediate benefit in the form of a strong tool to solve the optimization problem. Further, it provides a long-term benefit by identifying a system that has the potential to effectively solve other difficult optimization problems with a similar search space.

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

[2]  Ron Shonkwiler,et al.  Parallel Genetic Algorithms , 1993, ICGA.

[3]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  John H. Holland,et al.  Distributed genetic algorithms for function optimization , 1989 .

[5]  F. Herrera,et al.  Heterogeneous distributed genetic algorithms based on the crossover operator , 1997 .

[6]  Thomas Bäck,et al.  An Overview of Evolutionary Computation , 1993, ECML.

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Erik D. Goodman,et al.  The hierarchical fair competition (HFC) model for parallel evolutionary algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[10]  W Bossert,et al.  Mathematical optimization: are there abstract limits on natural selection? , 1967, The Wistar Institute symposium monograph.

[11]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[12]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[13]  John J. Grefenstette,et al.  ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS , 1995 .

[14]  Richard J. Enbody,et al.  Further Research on Feature Selection and Classification Using Genetic Algorithms , 1993, ICGA.

[15]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

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

[17]  Erik D. Goodman,et al.  Using Genetic Algorithms to Design Laminated Composite Structures , 1995, IEEE Expert.

[18]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[19]  Paul Bryant Grosso,et al.  Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model , 1985 .

[20]  Bernard Manderick,et al.  A Massively Parallel Genetic Algorithm: Implementation and First Analysis , 1991, ICGA.

[21]  Ian C. Parmee,et al.  Co-operative Evolutionary Strategies for Single Component Design , 1997, ICGA.

[22]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[23]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[24]  Enrique Alba,et al.  Parallel heterogeneous genetic algorithms for continuous optimization , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[25]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[26]  Reiko Tanese,et al.  Parallel Genetic Algorithms for a Hypercube , 1987, ICGA.

[27]  Maolin Tang.  A design pattern for Web-based parallel genetic algorithms , 2002, 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings..

[28]  Shigeyoshi Tsutsui,et al.  Forking Genetic Algorithm with Blocking and Shrinking Modes (fGA) , 1993, ICGA.

[29]  Günter Rudolph,et al.  Contemporary Evolution Strategies , 1995, ECAL.

[30]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[31]  Michael P. SanSoucie,et al.  Evolving High-Performance Evolutionary Computations for Space Vehicle Design , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[32]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[33]  Filippo Neri,et al.  A Parallel Genetic Algorithm for Concept Learning , 1995, ICGA.

[34]  Melanie Mitchell,et al.  Relative Building-Block Fitness and the Building Block Hypothesis , 1992, FOGA.

[35]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[36]  L. Darrell Whitley,et al.  Serial and Parallel Genetic Algorithms as Function Optimizers , 1993, ICGA.

[37]  Erik D. Goodman,et al.  Coarse-grain parallel genetic algorithms: categorization and new approach , 1994, Proceedings of 1994 6th IEEE Symposium on Parallel and Distributed Processing.

[38]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[39]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[40]  A. D. Bethke,et al.  Comparison of genetic algorithms and gradient-based optimizers on parallel processors : efficiency of use of processing capacity , 1976 .

[41]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[42]  Enrique Alba,et al.  A survey of parallel distributed genetic algorithms , 1999, Complex..

[43]  Vassilios Petridis,et al.  Co-operating Populations with Different Evolution Behaviours , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

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

[45]  Dana S. Richards,et al.  A Multi-Population Genetic Algorithm for Solving the K-Partition Problem on Hyper-Cubes , 1991, ICGA.

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