A hybrid evolutionary approach for solving constrained optimization problems over finite domains

A novel approach for the integration of evolution programs and constraint-solving techniques over finite domains is presented. This integration provides a problem-independent optimization strategy for large-scale constrained optimization problems over finite domains. In this approach, genetic operators are based on an arc-consistency algorithm, and chromosomes are arc-consistent portions of the search space of the problem. The paper describes the main issues arising in this integration: chromosome representation and evaluation, selection and replacement strategies, and the design of genetic operators. We also present a parallel execution model for a distributed memory architecture of the previous integration. We have adopted a global parallelization approach that preserves the properties, behavior, and fundamentals of the sequential algorithm. Linear speedup is achieved since genetic operators are coarse grained as they perform a search in a discrete space carrying out arc consistency. The implementation has been tested on a GRAY T3E multiprocessor using a complex constrained optimization problem.

[1]  Erik D. Goodman,et al.  Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems , 1997, Evolutionary Programming.

[2]  Jan Paredis,et al.  Coevolutionary computation , 1995 .

[3]  Christopher R. Houck,et al.  On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[4]  M. Rojas,et al.  Using the knowledge of the constraints network to design an evolutionary algorithm that solves CSP , 1996 .

[5]  Mats Carlsson,et al.  An Open-Ended Finite Domain Constraint Solver , 1997, PLILP.

[6]  Steven Minton,et al.  Minimizing Conflicts: A Heuristic Repair Method for Constraint Satisfaction and Scheduling Problems , 1992, Artif. Intell..

[7]  K. Al-Sultan,et al.  A Genetic Algorithm for the Set Covering Problem , 1996 .

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

[9]  James Bowen,et al.  Solving Constraint Satisfaction Problems Using a Genetic/Systematic Search Hybrid That Realizes When to Quit , 1995, ICGA.

[10]  Prithviraj Banerjee,et al.  A parallel simulated annealing algorithm for channel routing on a hypercube multiprocessor , 1988, Proceedings 1988 IEEE International Conference on Computer Design: VLSI.

[11]  J. Beasley,et al.  A genetic algorithm for the set covering problem , 1996 .

[12]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (3rd ed.) , 1996 .

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

[14]  Reinhard Männer,et al.  Implementation of Standard Genetic Algorithm on MIMD Machines , 1994, PPSN.

[15]  武藤 佳恭.  Neural network parallel computing , 1992 .

[16]  John J. Grefenstette,et al.  A Parallel Genetic Algorithm , 1987, ICGA.

[17]  Peter J. Stuckey,et al.  Programming with Constraints: An Introduction , 1998 .

[18]  Christopher M. Brown,et al.  Parallel genetic algorithms on distributed-memory architectures , 1993 .

[19]  Takeshi Yoshimura,et al.  Efficient Algorithms for Channel Routing , 1982, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[20]  Thomas C. Henderson,et al.  Arc and Path Consistency Revisited , 1986, Artif. Intell..

[21]  Abdollah Homaifar,et al.  Constrained Optimization Via Genetic Algorithms , 1994, Simul..

[22]  María Cristina Riff,et al.  Using the knowledge of the constraints network to design an evolutionary algorithm that solves CSP , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[23]  Elena Marchiori,et al.  Combining Constraint Processing and Genetic Algorithms for Constraint Satisfaction Problems , 1997, ICGA.

[24]  Oliver Vornberger,et al.  Hybrid genetic algorithms for constrained placement problems , 1997, IEEE Trans. Evol. Comput..

[25]  Wu-Shiung Feng,et al.  A new efficient approach to multilayer channel routing problem , 1992, [1992] Proceedings 29th ACM/IEEE Design Automation Conference.

[26]  Wm Leler,et al.  Constraint programming languages , 1987 .

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

[28]  Pascal Van Hentenryck.  The OPL optimization programming language , 1999 .

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

[30]  Xingzhao Liu,et al.  Genetic Channel Router , 1994 .

[31]  Neng-Fa Zhou.  Channel Routing with Constraint Logic Programming and Delay , 1996, IEA/AIE.

[32]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[33]  Z. Michalewicz,et al.  Genocop III: a co-evolutionary algorithm for numerical optimization problems with nonlinear constraints , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[34]  Runhe Huang,et al.  Implementing the Genetic Algorithm on Transputer Based Parallel Processing Systems , 1990, PPSN.

[35]  E. L. Lawler,et al.  Branch-and-Bound Methods: A Survey , 1966, Oper. Res..

[36]  A. E. Eiben,et al.  Self-adaptivity for constraint satisfaction: learning penalty functions , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[37]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.

[38]  Pascal Van Hentenryck,et al.  A Generic Arc-Consistency Algorithm and its Specializations , 1992, Artif. Intell..

[39]  James Bowen,et al.  Solving constraint satisfaction problems using hybrid evolutionary search , 1998, IEEE Trans. Evol. Comput..

[40]  Jean-francois Puget,et al.  A C++ implementation of CLP , 1997 .

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