Successful Application of Evolutionary Algorithms in Engineering Design

Abstract Evolutionary or bionic strategies have proven to be powerful tools in many optimisation studies. Starting with some parent generations, producing sets of children, selecting the best children to be new parents yields impressive improvements of the objective when used with some experience and sufficient equipment. During some years of research the parameters of evolutionary optimisation have been investigated. Many successful applications showed where and how to use it. Especially in the case of objective functions with some or many local maxima, evolutionary approaches may propose solutions which gradient based optimisation would hardly find. When used with a large number of optimisation parameters, evolutionary methods seem to be superior to other strategies, as the chances to find good proposals within an acceptable number of trials and within affordable time are much higher. Nevertheless, evolutionary approaches like all optimisation methods require large numbers of studies of individual solutions. The computer power necessary to apply these strategies should not be underestimated. Even with today low cost and high availability of computers, the time to solve problems may be surprisingly long. So all ways of parallel processing, using single computers with many processors or clusters of many computers may speed up the time to do the optimisation. The basic terms of the method are outlined, some problems discussed, some examples given and some proposals made, how to use evolutionary methods in engineering optimisation. Finally some warnings are given trying to prevent potential users from non-realistic expectations. Optimisation is a difficult and consuming process. This holds for evolutionary optimisation as well.