Robust Encodings in Genetic Algorithms

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

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

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

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

[5]  Lawrence Davis,et al.  Genetic Algorithms and Communication Link Speed Design: Theoretical Considerations , 1987, ICGA.

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

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

[8]  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.

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

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

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

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

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

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

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

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

[17]  J. L. Ribeiro Filho The GAME system (Genetic Algorithms Manipulation Environment) , 1994 .

[18]  Lawrence Davis,et al.  Genetic Algorithms and Communication Link Speed Design: Constraints and Operators , 1987, ICGA.

[19]  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.

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

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