An integrated framework of hybrid evolutionary computations
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Comparison studies of LS_SVM and SVM on modeling for fermentation processes
ICNC'09 Proceedings of the 5th international conference on Natural computation
An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems
Expert Systems with Applications: An International Journal
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Most methods in evolutionary computation are biologically inspired by chromosome crossover and mutation, two of the main sources of genetic variability in biological populations, as well as in genetic algorithms. In fact, this is a very important feature of the biological populations and their counterpart in genetic algorithms since the efficiency of the Darwinian natural selection depends on the degree of genetic variation that is achieved with the genetic mechanism or operator responsible for the population variability. Furthermore, in Nature, several other genetic mechanisms are used by populations as sources of variability such as bacterial conjugation, that is the transfer of genetic material between bacteria. In this paper, we introduce a biologically inspired conjugation operator simulating the genetic mechanism exhibited by bacterial colonies. The efficiency of the bacterial conjugation operator is illustrated designing with a genetic algorithm based on this operator an AM radio receiver, optimizing the main features of the electronic components of the AM radio circuit, as well as those of the radio enclosure.