Efficient Discretization Scheduling In Multiple Dimensions

There is a tradeoff between speed and accuracy in fitness evaluations when various discretization sizes are used to estimate the fitness. This paper introduces discretization scheduling, which varies the size of the discretization within the GA, and compares this method to using a constant discretization. It will be shown that when scheduling the discretization, less computation time is used without sacrificing solution quality. Fitness functions whose cost and accuracy vary because of discretization errors from numerical integration are considered, and the speedup achieved from using efficient discretizations is predicted and shown empirically.

[1]  Franz Rothlauf,et al.  Representations for genetic and evolutionary algorithms , 2002, Studies in Fuzziness and Soft Computing.

[2]  David E. Goldberg,et al.  Finite Markov Chain Analysis of Genetic Algorithms , 1987, ICGA.

[3]  Bernhard Sendhoff,et al.  On Evolutionary Optimization with Approximate Fitness Functions , 2000, GECCO.

[4]  D. Goldberg,et al.  Domino convergence, drift, and the temporal-salience structure of problems , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[5]  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.

[6]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[7]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[8]  John J. Grefenstette,et al.  Genetic algorithms in noisy environments , 1988, Machine Learning.

[9]  Erik D. Goodman,et al.  Optimal design of laminated composite structures using coarse-grain parallel genetic algorithms , 1994 .

[10]  David E. Goldberg,et al.  Efficient evaluation relaxation under integrated fitness functions , 2001 .

[11]  David E. Goldberg,et al.  Time Complexity of genetic algorithms on exponentially scaled problems , 2000, GECCO.

[12]  M. Kimura.  Difiusion models in population genetics , 1964 .

[13]  David E. Goldberg,et al.  Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise , 1996, Evolutionary Computation.

[14]  Benjamin W. Wah,et al.  Scheduling of Genetic Algorithms in a Noisy Environment , 1994, Evolutionary Computation.

[15]  Nozomu Kogiso,et al.  Genetic algorithms with local improvement for composite laminate design , 1993 .

[16]  Jens von Wolfersdorf,et al.  Shape Optimization of Cooling Channels Using Genetic Algorithms , 1997 .

[17]  R. Haftka,et al.  Optimization of laminate stacking sequence for buckling load maximization by genetic algorithm , 1993 .

[18]  Andy J. Keane,et al.  Metamodeling Techniques For Evolutionary Optimization of Computationally Expensive Problems: Promises and Limitations , 1999, GECCO.