. Most real-world applications operate in dynamic environments. In such environments often it is necessary to modify the current solution due to various changes in the environment (e.g., machine breakdowns, sickness of employees, etc). Thus it is important to investigate properties of adaptive algorithms which do not require re-start every time a change is recorded. In this paper non-stationary problems (i.e., problems, which change in time) are discussed. We describe different types of changes in the environment. A new model for non-stationary problems and a classification of these problems by the type of changes is proposed. A brief review of existing applied measures of obtained results is also presented. 1 Introduction Most optimization algorithms assume static objective function; they search for a nearoptimum solution with respect to some fixed measure (or set of measures), whether it is maximization of profits, minimization of a completion time for some tasks, minimiza...

Evolutionary algorithms are frequently applied to dynamic optimization problems in which the objective varies with time. It is desirable to gain an improved understanding of the influence of different genetic operators and of the parameters of a strategy on its tracking performance. An approach that has proven useful in the past is to mathematically analyze the strategy's behavior in simple, idealized environments. The present paper investigates the performance of a multiparent evolution strategy that employs cumulative step length adaptation for an optimization task in which the target moves linearly with uniform speed. Scaling laws that quite accurately describe the behavior of the strategy and that greatly contribute to its understanding are derived. It is shown that in contrast to previously obtained results for a randomly moving target, cumulative step length adaptation fails to achieve optimal step lengths if the target moves in a linear fashion. Implications for the choice of population size parameters are discussed.

Genetic Algorithms (GAs) with gene dependent mutation probability applied to non-stationary optimization problems are investigated in this paper. In the problems studied here, the fitness function changes during the search carried out by the GA. In the GA investigated, each gene is associated with an independent mutation probability. The knowledge obtained during the evolution is utilized to update the mutation probabilities. If the modification of a set of genes is useful when the problem changes, the mutation probabilities of these genes are increased. In this way, the search in the solution space is concentrated into regions associated with the genes with higher mutation probabilities. The class of non-stationary problems where this GA can be interesting and its limitations are investigated.

Abstract not found

In this article, the authors investigate the application of Genetic Algorithms (GAs) with gene dependent mutation probability to the training of Artificial Neural Networks (ANNs) in non-stationary problems. In the problems studied, the function mapped by an ANN changes during the search carried out by the GA. In the GA proposed, each gene is associated with an independent mutation probability. The knowledge obtained during the evolution is used to update the mutation probabilities. If the modification of a set of genes is useful when the problem changes its profile, the mutation probabilities of these genes are increased. As a result, the search is concentrated into regions associated with genes presenting higher mutation probabilities.

A new method for optimizing complex engineering designs is presented that is based on the Learnable Evolution Model (LEM), a recently developed form of non-Darwinian evolutionary computation. Unlike conventional Darwinian-type methods that execute an unguided evolutionary process, the proposed method, called LEMd, guides the evolutionary design process using a combination of two methods, one involving computational intelligence and the other involving encoded expert knowledge. Specifically, LEMd integrates two modes of operation, Learning Mode and Probing Mode. Learning Mode applies a machine learning program to create new designs through hypothesis generation and instantiation, while Probing Mode creates them by applying expertsuggested design modification operators tailored to the specific design problem. The LEMd method has been used to implement two initial systems, ISHED1 and ISCOD1, specialized for the optimization of evaporators and condensers in cooling systems, respectively. The designs produced by these systems matched or exceeded in performance the best designs developed by human experts. These promising results and the generality of the presented method suggest that LEMd offers a powerful new tool for optimizing complex engineering systems.