Similar to Genetic Algorithms, Evolution Strategies (ESs) are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems. The development of Evolution Strategies from the first mutation--selection scheme to the refined (¯,)--ES including the general concept of self--adaptation of the strategy parameters for the mutation variances as well as their covariances are described. 1 Introduction The idea to use principles of organic evolution processes as rules for optimum seeking procedures emerged independently on both sides of the Atlantic ocean more than two decades ago. Both approaches rely upon imitating the collective learning paradigm of natural populations, based upon Darwin's observations and the modern synthetic theory of evolution. In the USA Holland introduced Genetic Algorithms in the 60ies, embedded into the general framework of adaptation [Hol75]. He also mentioned the applicability to parameter optimization which was fir...

In this paper we review parallel implementations of some stochastic global optimization methods on MIMD computers. Moreover, we present a new parallel version of an Evolutionary Algorithm for global optimization, where the inherent parallelism can be scaled to obtain a reasonable processor utilization. For this algorithm the convergence to the global optimum with probability one can be assured. Test results concerning speed up and reliability are given. 1 Introduction Many real world problems in engineering and economics can be formulated as optimization problems, in which the objective function is multimodal, i.e. the problem possesses many local minima. Compared to the number of methods designed to determine a local minimum, there are only a few methods which attempt to find the global minimum (see [52] for a survey). Although there are some special cases where the global optimum can be found (see [26]) the general case is unsolvable. This paper will be restricted to the more gener...