Abstract
This paper presents a highly parallel genetic algorithm, designed for concept induction in propositional and First Order logics. The parallel architecture is an adaptation to set covering problems of the diffusion model developed for optimization problems.
Moreover, the algorithm exhibits other two important methodological novelties related to the Evolutional Computation field. First, it combines niches and species formation with co-evolution in order to learn multimodal concepts. This is done by integrating the Universal Suffrage selection operator with the co-evolution model recently proposed in the literature. Second, it makes use of a new set of genetic operators, which maintain diversity in the population.
The experimental comparison with previous systems, not using coevolution and based on traditional genetic operators, shows a substantial improvement in the effectiveness of the genetic search.
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References
C. Anglano, A. giordana, G. Lo Bello, and L. Saitta. A network genetic algorithm for concept learning. In Seventh Int. Conference on Genetic Algorithms, East Lansing, MI, 1997. To appear.
K.A. De Jong, W.M. Spears, and F.D. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13:161–188, 1993.
A. Giordana and F. Neri. Search-intensive concept induction. Evolutionary Computation, 1996. Winter Issue.
D.E. Goldberg. Genetic Algorithms. Addison-Wesley, Readings, MA, 1989.
D.E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In Int. Conf. on Genetic Algorithms, pages 41–49, Cambridge, MA, 1987. Morgan Kaufmann.
D.P. Greene and S.F. Smith. Competition-based induction of decision models from examples. Machine Learning, 13:229–258, 1993.
P. Husbands and F. Mill. Co-evolving parasites improve simulated evolution as an optimization procedure. In 4th Int. Conf. on Genetic Algorithms, pages 264–270, Fairfax, VA, 1991. Morgan Kaufmann.
C.Z. Janikow. A knowledge intensive genetic algorithm for supervised learning. Machine Learning, 13:198–228, 1993.
R. Michalski. A theory and methodology of inductive learning. In J. Carbonell R. Michalski and T. Mitchell, editors, Machine Learning: An AI Approach, pages 83–134, Los Altos, CA, 1983. Morgan Kaufmann.
M.A. Potter, K.A. De Jong, and J.J. Grefenstette. A coevolutionary approach to learning sequential decision rules. In 6th Int. Conf. on Genetic Algorithms, pages 366–372, Pittsburgh, PA, 1995. Morgan Kaufmann.
G. Syswerda. Uniform crossover in genetic algorithms. In 3th Int. Conf. on Genetic Algorithms, pages 2–9, Fairfax, VA, 1989. Morgan Kaufmann.
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© 1997 Springer-Verlag Berlin Heidelberg
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Giordana, A., Saitta, L., Lo Bello, G. (1997). A coevolutionary approach to concept learning. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1997. Lecture Notes in Computer Science, vol 1325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63614-5_25
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DOI: https://doi.org/10.1007/3-540-63614-5_25
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