This paper gives a concise overview of evolutionary algorithms for
multiobjective optimization. A substantial number of evolutionary computation
methods for multiobjective problem solving has been proposed so far, and an
attempt of unifying existing approaches is here presented. Based on a
fine-grained decomposition and following the main issues of fitness assignment,
diversity preservation and elitism, a conceptual global model is proposed and
is validated by regarding a number of state-of-the-art algorithms as simple
variants of the same structure. The presented model is then incorporated into a
general-purpose software framework dedicated to the design and the
implementation of evolutionary multiobjective optimization techniques:
ParadisEO-MOEO. This package has proven its validity and flexibility by
enabling the resolution of many real-world and hard multiobjective optimization
problems
