Abstract
In this paper we study the influence of the exploration/exploitation balance on the performances of a real binary/like genetic algorithm in automatically generating fuzzy knowledge bases from a set of numerical data. The influence is explored through different scheduling of crossover strategies throughout the evolution process. The aim of this paper is to prove the influence of a good balance between exploration and exploitation levels on the performances of the optimization algorithm used, along with the influence of a good definition of the early stages versus the late stages of the evolution.
Original language | English |
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Title of host publication | Annual Meeting of the North American Fuzzy Information Processing Society (IEEE) |
Volume | 1 |
Publication date | 2004 |
Pages | 401-406 |
Publication status | Published - 2004 |
Externally published | Yes |
Event | 2004 Annual Meeting of the North American Fuzzy Information Processing Society - Banff, Canada Duration: 27 Jun 2004 → 30 Jun 2004 |
Conference
Conference | 2004 Annual Meeting of the North American Fuzzy Information Processing Society |
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Country/Territory | Canada |
City | Banff |
Period | 27/06/2004 → 30/06/2004 |