Heinz Mühlenbein

Learn More
The Breeder Genetic Algorithm (BGA) is based on the equation for the response to selection. In order to use this equation for prediction , the variance of the tness of the population has to be estimated. For the usual sexual recombination the computation can be diicult. In this paper we shortly state the problem and investigate several modiications of(More)
In this paper a new genetic algorithm called the Breeder Genetic Al gorithm BGA is introduced The BGA is based on arti cial 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 Di erent mutation(More)
The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population's fitness to the standard deviation of its fitness, as well as to the parameters(More)
In this paper the optimization of additively decomposed discrete functions is investigated. For these functions genetic algorithms have exhibited a poor performance. First the schema theory of genetic algorithms is reformulated in probability theory terms. A schema deenes the structure of a marginal distribution. Then the conceptual algorithm BEDA is(More)
The Factorized Distribution Algorithm (FDA) is an evolutionary algorithm which combines mutation and recombination by using a distribution. The distribution is estimated from a set of selected points. In general, a discrete distribution defined for n binary variables has 2(n) parameters. Therefore it is too expensive to compute. For additively decomposed(More)
Miihlenbein, H., M. Schomisch and J. Born, The parallel genetic algorithm as function optimizer, Parallel Computing 17 (1991) 619-632. In this paper, the parallel genetic algorithm PGA is applied to the optimization of continuous functions. The PGA uses a mixed strategy. Subpopulations try to locate good local minima. If a subpopulation does not progress(More)
Abs t rac t In this paper we introduce our asynchronous parallel genetic algorithm ASPARAGOS. The two major extensions compared to genetic algorithms are the following. First, individuals live on a 2-D grid and selection is done locally in the neighborhood. Second, each individual does local hill climbing. The rationale for these extensions is discussed(More)