In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented which is derived from quantitative genetics. The model is used to predict the behavior of the BGA for simple test functions. Different mutation schemes are compared by computing the expected progress to the solution. The numerical performance of the BGA is demonstrated on a test suite of multimodal functions. The number of function evaluations needed to locate the optimum scales only as n ln(n) where n is the number of parameters. Results up to n = 1000 are reported.

Evolutionary algorithms which model natural evolution processes have been successfully used for optimization. Theoretical explanations why and how the algorithms work have been less successful. In this paper evolutionary algorithms are considered as random search methods. The genetic operators mutation and recombination are evaluated according to the measure expected progress. Mutation is analyzed by Markov chains and probability theory. Recombination is investigated together with selection by methods of quantitative genetics. Furthermore it is shown that hillclimbing improves the efficiency of the search in ruggged landscapes. The theoretical results are used in two evolutionary algorithms - the parallel genetic algorithm PGA and the breeder genetic algorithm BGA. The PGA models natural evolution which self-organizes itself, the BGA models rational controlled evolution by a virtual breeder. The efficiency of both algorithms is shown with numerical examples. Results of a real world pro...

BIOLOGÍA ANIMAL Y VEGETAL 103 MATEMÁTICAS 109 QUÍMICA 117 AGRICULTURA, PRATICULTURA Y CONSERVACIÓN DE FORRAJES 119

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