The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a time-changing fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison.

Several evolutionary algorithms have been proposed for the satisfiability problem. We review the solution representations suggested in literature and choose the most promising one the bit string representation for further evaluation. An empirical comparison on commonly used benchmarks is presented for the most successful evolutionary algorithms and for WSAT, a prominent local search algorithm for the satisfi-ability problem. The key features of successful evolutionary algorithms are identified, thereby providing useful methodological guidelines for designing new heuristics. Our results indicate that evolutionary algorithms are competitive to WSAT.