Multidimensional Optimization with a Fuzzy Genetic Algorithm

We present a new heuristic method to approximate the set of Pareto-optimal solutions in multicriteria optimization problems. We use genetic algorithms with an adaptive selection mechanism. The direction of the selection pressure is adapted to the actual state of the population and forces it to explore a broad range of so far undominated solutions. The adaptation is done by a fuzzy rule-based control of the selection procedure and the fitness function. As an application we present a timetable optimization problem where we used this method to derive cost-benefit curves for the investment into railway nets. These results show that our fuzzy adaptive approach avoids most of the empirical shortcomings of other multiobjective genetic algorithms.