ABSTRACT
Evolutionary Algorithms have proved to be a powerful tool for solving complex optimization problems. The underlying physical and biological strategies can be described by the Master Equation formalism. Combination of both strategies creates a new basic class of Evolutionary Algorithms where robustness as well as performance of the optimization process improve substantially. To characterize the complexity of an optimization problem one may introduce a measure which remains invariant with regard to different schemes of representation: the density of states. It is the probability that an arbitrary chosen state has a certain fitness value. The knowledge of this probability makes it possible to estimate the optimal fitness value and the computational effort to find a better solution of the problem. A general method is presented which allows to approximate the density of states during the optimization process. This is demonstrated for frustrated sequences, road networks and especially for the secondary structures of RNA.
