A non-generational genetic algorithm for multiobjective optimization

In this paper a non-generational genetic algorithm for multiobjective optimization problems is proposed. For each element in the population a domination count is defined together with a neighborhood density measure based on a sharing function. Those two measures are then nonlinearly combined in order to define the individual's fitness. Numerical experiments with four test-problems taken from the evolutionary multiobjective literature are performed and the results are compared with those obtained by other evolutionary techniques.