Multi-Combinative Strategy to Avoid Premature Convergence in Genetically-Generated Fuzzy Knowledge Bases

Sofiane Achiche, Luc Baron, Marek Balazinski

Research output: Contribution to journalJournal articleResearchpeer-review


A growing number of industrial fields are concerned by complex and multi-objective problems. For this kind of problems, optimal decision making is critical. Decision support systems using fuzzy logic are often used to deal with complex and large decision making problems. However the main drawback is the need of an expert to manually construct the knowledge base. The use of genetic algorithms proved to be an effective way to solve this problem. Genetic algorithms model life evolution strategy using the Darwin theory. A main problem in genetic algorithms is the premature convergence and the last enhancements in order to solve this problem include new multi-combinative reproduction techniques. There are two principal ways to perform multi-combinative reproduction within a genetic algorithm, namely the Multi-parent Recombination, Multiple Crossover on Multiple Parents (MCMP); and the Multiple Crossovers Per Couple (MCPC). Both techniques try to take the most of the genetic information contained in the parents. This paper explores the possibility to decrease premature convergence in a real/binary like coded genetic algorithm (RBCGA) used in automatic generation of fuzzy knowledge bases (FKBs). The RBCGA uses several crossover mechanisms applied to the same couple of parents. The crossovers are also combined in different ways creating multiple offspring from the same parent genes. The large family concept and the variation of the crossovers should introduce diversity and variation in otherwise prematurely converged populations and hence, keeping the search process active.
Original languageEnglish
JournalJournal of Theoretical and Applied Mechanics
Issue number3
Pages (from-to)417-444
Publication statusPublished - 2004
Externally publishedYes


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