We propose a new solution to the problem of positioning base station transmitters of a mobile phone network and assigning frequencies to the transmitters, both in an optimal way. Since an exact solution cannot be expected to run in polynomial time for all interesting versions of this problem (they are all NP-hard), our algorithm follows a heuristic approach based on the evolutionary paradigm. For this evolution to be efficient, that is at the same time goal-oriented and sufficiently random, problem specific knowledge is embedded in the operators. The problem requires both the minimization of the cost and of the channel interference. We examine and compare two standard multiobjective techniques and a new algorithm, the steady state evolutionary algorithm with Pareto tournaments (stEAPT). One major finding of the empirical investigation is a strong influence of the choice of the multiobjective selection method on the utility of the problem-specific recombination leading to a significant difference in the solution quality. This work has been partially funded by project TH-46/02-1.

During the last years, cooperating coevolutionary algorithms could improve convergence of several optimization benchmarks significantly by placing each dimension of the search space in its own subpopulation. Though, their general applicability is restricted by problems with epistatic links between problem dimensions -a major obstacle in cooperating coevolutionary function optimization. This work presents first preliminary studies on a technique to recognize epistatic links in problems and selfadapt the algorithm in such a way that populations with interrelated dimensions are merged to a common population.

This article investigates systematically the utility of performance measures in non-stationary environments. Three characteristics for describing the goals of a dynamic adaptation process are proposed: accuracy, stability, and recovery. This examination underpins the usage of the best fitness value as a basis for measuring the three characteristics in scenarios with moderate changes of the best fitness value. However, for dynamic problems without coordinate transformations all considered fitness based measures exhibit severe problems. In case of the recovery, a newly proposed window based performance measure is shown to be best as long as the accuracy level of the optimization is rather high. 2 A Classification of Dynamic Problems There are only few very coarse grained classifications distinguishing alternating (or cyclic) problems, problems with changing morphology, drifting landscapes, and abrupt and discontinuous problems (cf. Collard, Escazut, & Gaspar,

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