Robust encodings in genetic algorithms: a survey of encoding issues

Problems of encoding brittleness have been observed in the genetic algorithm (GA) literature, where slightly different problems require completely different genetic encodings for good solutions to be found. As research continues into GA encoding schemes the idea of encoding robustness becomes more important. A robust encoding is one which will be effective for a wide range of problem instances that it was designed for. A robust encoding will also be amenable to modification or extension to solve different problem types. This paper is a survey of some of the practical and theoretical considerations vital to the construction of a more robust encoding which will allow the GA to solve a broader range of problem types.

[1]  James P. Cohoon,et al.  Genetic Placement , 1987, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[2]  W. Stansfield,et al.  Theory and problems of genetics. , 1969 .

[3]  S. Ronald.  Finding multiple solutions with an evolutionary algorithm , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[4]  A. Mason.  A non-linearity measure of a problem's crossover suitability , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[5]  Peter Ross,et al.  A Promising Genetic Algorithm Approach to Job-Shop SchedulingRe-Schedulingand Open-Shop Scheduling Problems , 1993, ICGA.

[6]  J. Gero,et al.  Improving Pareto Optimal Designs Using Genetic Algorithms , 1995 .

[7]  Ralf Bruns,et al.  Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling , 1993, ICGA.

[8]  Charles E. Taylor.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Complex Adaptive Systems.John H. Holland , 1994 .

[9]  B. R. Fox,et al.  Genetic Operators for Sequencing Problems , 1990, FOGA.

[10]  Z. Michalewicz,et al.  A genetic algorithm for the linear transportation problem , 1991, IEEE Trans. Syst. Man Cybern..

[11]  J. Asenstorfer,et al.  Representational redundancy in evolutionary algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[12]  Kenneth A. De Jong,et al.  Using Genetic Algorithms to Solve NP-Complete Problems , 1989, ICGA.

[13]  Kenneth A. De Jong,et al.  Genetic algorithms: A 10 Year Perspective , 1985, ICGA.

[14]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[15]  Ralph R. Martin,et al.  Reducing Epistasis in Combinatorial Problems by Expansive Coding , 1993, ICGA.

[16]  Dana S. Richards,et al.  Floorplan design using distributed genetic algorithms , 1988, [1988] IEEE International Conference on Computer-Aided Design (ICCAD-89) Digest of Technical Papers.

[17]  Sushil J. Louis,et al.  Designer Genetic Algorithms: Genetic Algorithms in Structure Design , 1991, ICGA.

[18]  Robert G. Reynolds,et al.  Evolutionary computation: Towards a new philosophy of machine intelligence , 1997 .

[19]  N. Sheibani,et al.  Paris , 1894, The Hospital.

[20]  C. G. Shaefer,et al.  The ARGOT Strategy: Adaptive Representation Genetic Optimizer Technique , 1987, ICGA.

[21]  L. Darrell Whitley,et al.  Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator , 1989, International Conference on Genetic Algorithms.

[22]  FEDERICO DELLA CROCE,et al.  A genetic algorithm for the job shop problem , 1995, Comput. Oper. Res..

[23]  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.