Abstract
This paper presents a highly parallel genetic algorithm, called G-NET, designed for concept induction in propositional and first order logics. As well as other systems oriented to the same task, G-NET exploits niches and species for learning multimodal concepts; on the other hand it deeply differs from other systems because of the distributed architecture, which totally eliminates the concept of common memory. A simulator of the system, designed in order to check the possibility of exploiting parallel processing, is evaluated on a standard benchmark. The experimental results show that a multi-processor implemented with standard technology could reach speed-up of thousands of times.
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© 1996 Springer-Verlag Berlin Heidelberg
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Giordana, A., Neri, F., Saitta, L. (1996). A fine-grained parallel evolutionary program for concept induction. In: Raś, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_142
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DOI: https://doi.org/10.1007/3-540-61286-6_142
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