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 for set covering problems, of the diffusion model developed for optimization.
The algorithm exhibits other two important methodological novelties related to Evolutionary Computation. First, it combines niches and species formation with coevolution, in order to learn multimodal concepts. This is done by integrating the Universal Suffrage selection operator with the coevolution 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.
Chapter PDF
References
Augier, S., Venturini, G., and Kodratoff, Y. Learning First Order Logic Rules with a Genetic Algorithm. In Proc. Of the First International Conference on Knowledge Discovery and Data Mining (1995).
DeJong, K., Spears, W., and Gordon, F. Using genetic algorithms for concept learning. Machine Learning Journal, 13 (1993), 161–188.
Giordana, A., and Neri, F. Search-intensive concept induction. Evolutionary Computation Journal (1996), Winter, 1995.
Goldberg, D.Genetic Algorithms. Addison-Wesley, Reading, MA, 1989.
Goldberg, D., and Richardson, J. Genetic algorithms with sharing for multimodal function optimization. In Int. Conf. on Genetic Algorithms (Cambridge, MA, 1987), Morgan Kaufmann, pp. 41–49.
Greene, D., and Smith, S. Competition-based induction of decision models from examples. Machine Learning Journal, 13 (1993), 229–258.
Husbands, P., and Mill, F. Co-evolving parasites improve simulated evolution as an optimization procedure. In 4th Int. Conf. on Genetic Algorithms (Fairfax, VA, 1991), Morgan Kaufmann, pp. 264–270.
Janikow, C. A knowledge intensive genetic algorithm for supervised learning. Machine Learning Journal, 13 (1993), 198–228.
Michalski, R. A theory and methodology of inductive learning. In Machine Learning: An AI Approach (Los Altos, CA, 1983), J. C. R. Michalski and T. Mitchell, Eds., Morgan Kaufmann, pp. 83–134.
Potter, M., DeJong, K., and Grefenstette, J. A coevolutionary approach to learning sequential decision rules. In Int. Conf. on Genetic Algorithms (Pittsburgh, PA, 1995), Morgan Kaufmann, pp. 366–372.
Syswerda, G. Uniform crossover in genetic algorithms. In 3th Int. Conf. on Genetic Algorithms (Fairfax, VA, 1989), Morgan Kaufmann, pp. 2–9.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Anglano, C., Giordana, A., Lo Bello, G., Saitta, L. (1998). Coevolutionary, distributed search for inducing concept descriptions. In: Nédellec, C., Rouveirol, C. (eds) Machine Learning: ECML-98. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026703
Download citation
DOI: https://doi.org/10.1007/BFb0026703
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64417-0
Online ISBN: 978-3-540-69781-7
eBook Packages: Springer Book Archive