Combining model-based and genetics-based offspring...

Details Combining model-based and genetics-based offspring... Combining model-based and genetics-based offspring...

2007-01-16

2010-06-15

Sparks, Donald (Department: 2129)

Data processing: artificial intelligence

Machine learning

Genetic algorithm and genetic programming system

Reexamination Certificate

active

07739206

ABSTRACT:
A system and method for combining the model-based and genetics-based methods are combined according to a convergence criterion. When the population is not converged, the genetics-based approach is used, and when the population is converged, the model-based method is used to generate offspring. The algorithm benefits from using a model-based offspring generation only when the population shows a certain degree of regularity, i.e., converged in a stochastic sense. In addition, a more sophisticated method to construct the stochastic part of the model can be used. Also a biased Gaussian noise (the mean of the noise is not zero), as well as a white Gaussian noise (the mean of the noise is zero) can be preferably used for the stochastic part of the model.

REFERENCES:
Mario Costa and Edmondo Minisci, “MOPED: a Multi-Objective Parzen-based Estimation of Distribution algorithm for continuous problems”, Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, (pp. 282-294), Faro, Portugal.
European Search Report, EP 06001449.5, Jul. 12, 2006, 8 Pages.
Zhou, A. et al., “A Model-Based Evolutionary Algorithm for Bi-objective Optimization,” IEEE Congress on Evolutionary Computation, 2005, pp. 2568-2575, vol. 3, Piscataway, New Jersey, U.S.A.
Pena, J. M. et al., “GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms,” The 17thInternational Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, 2004, pp. 361-371, Springer-Verlag Berlin Heidelberg.
Zhang, Q. et al., “An Evolutionary Algorithm With Guided Mutation for the Maximum Clique Problem,” IEEE Transactions on Evolutionary Computation, Apr. 2005, pp. 192-200, vol. 9, No. 2.
Oh, S. et al., “Implicit Rile-Based Fuzzy-Neural Networks Using the Identification Algorithm of GA Hybrid Scheme Based on Information Granulation,” Advanced Engineering Informatics, Jul. 2002, pp. 247-263, vol. 16. No. 4.
Kim, H. et al., “An Efficient Genetic Algorithm With Less Fitness Evaluation by Clustering,” IEEE Evolutionary Computation, May 2001, pp. 887-894, vol. 2, Piscataway, New Jersey, U.S.A.

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