Parallel combinatorial optimization with evolutionary cooperation between processors

An evolutionary computation approach is used to learn online the rules that allow the processors in a parallel platform to cooperate by interchanging the local optima that they find while they concurrently explore different zones of the solution space. The cooperation of processors can greatly benefit the resolution of combinatorial optimization problems by decreasing their runtimes, by increasing the quality of the solutions obtained, or both. Moreover, as parallel computers are more and more accessible, the application of parallel processing to solve these problems becomes a practical and interesting alternative. As an example, a parallel optimization algorithm based on Boltzmann Machine has been used for a detailed description and evaluation of the proposed cooperation approach.

[1]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[2]  John J. Greffenstette,et al.  A System for Learning Control Strategies with Genetic Algorithms , 1989 .

[3]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .

[4]  Vangelis Th. Paschos,et al.  On the Approximation of NP-Complete Problems by Using the Boltzmann Machine Method: The Cases of Some Covering and Packing Problems , 1991, IEEE Trans. Computers.

[5]  Edward W. Felten,et al.  Large-step markov chains for the TSP incorporating local search heuristics , 1992, Oper. Res. Lett..

[6]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[7]  Heinz Mühlenbein,et al.  Strategy Adaption by Competing Subpopulations , 1994, PPSN.

[8]  David E. Culler,et al.  A case for NOW (networks of workstation) , 1995, PODC '95.

[9]  John J. Grefenstette,et al.  A Coevolutionary Approach to Learning Sequential Decision Rules , 1995, ICGA.

[10]  Helena Ramalhinho Dias Lourenço,et al.  Job-shop scheduling: Computational study of local search and large-step optimization methods , 1995 .

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

[12]  David E. Culler,et al.  A case for NOW (networks of workstation) , 1995, PODC '95.

[13]  Bruce A. Whitehead,et al.  Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction , 1996, IEEE Trans. Neural Networks.

[14]  M. Sipper.  Co-evolving non-uniform cellular automata to perform computations , 1996 .

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

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

[17]  Xin Yao,et al.  A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

[18]  Marco Russo,et al.  FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling , 1998, IEEE Trans. Fuzzy Syst..

[19]  R. Paul Wiegand,et al.  Applying Diffusion to a Cooperative Coevolutionary Model , 1998, PPSN.