A simple, non-generational genetic algorithm based on moving averages for multiobjective optimization
Josí A. Ramírez-Ruíz, Luis M. Fernéndez-Carrasco, Manuel Valenzuela-Rendón, Eduardo Uresti-Charre · 2009
This paper proposes a new non-generational genetic algorithm for multiobjective optimization. The novelty in this approach is the use of a moving average to evaluate two things: the number of dominated individuals and the sharing function. Moreover, the proposed solution does less number of comparisons when compared with similar techniques and approaches. This solution has been evaluated on three different problems that are found in the literature and compared to other approaches to tackle them. The results that one sees point to the idea that the proposed algorithm is a feasible and simpler way to deal with multiobjective problems.