Micro-Genetic algorithm with fuzzy selection of operators for multi-Objective optimization: μFAME

We propose a new accurate Micro Genetic Algorithm (mu GA) for multi-objective optimization problems that we call Micro-FAME (or mu FAME). The distinctive feature of mu FAME with respect to the other existing multi-objective algorithms in the literature is its high elitism and fast convergence, produced by the application of the evolution directly on the Pareto front approximation. This means that it has a bounded population of variable size, determined by the number of non-dominated solutions found so far. In mu FAME (and generally in mu GAs) the premature convergence problem acquires especial relevance, and generating and maintaining solutions diversity is of extreme importance to deal with it. This is especially significant in the multi-objective field, where the algorithm looks for a wide and diverse set of non-dominated solutions. In mu FAME, the diversity loss that high elitism usually produces is overcome by the decisions driven by the Fuzzy Inference System (FIS), that chooses the most appropriate operators to apply during the evolution to promote both diversity and accuracy of solutions. The algorithm arises as a suitable solution for problems with a computationally heavy fitness function, because it is able to quickly achieve good quality local optimal solutions, but it is also highly competitive in long runs. Experiments reveal a great performance of mu FAME according to several convergence related metrics, when comparing against seven state-of-the-art algorithms on a large set of benchmark problems, as well as on a real-world engineering one with an expensive fitness function.

Automated summary

mu FAME is a new accurate Micro Genetic Algorithm proposed for multi-objective optimization problems, featuring high elitism and fast convergence achieved by directly applying evolution on the Pareto front approximation. It utilizes a Fuzzy Inference System to overcome diversity loss caused by high elitism and promote both diversity and accuracy of solutions. The algorithm is suitable for problems with computationally heavy fitness functions and exhibits great performance in comparison with state-of-the-art algorithms on various benchmark problems.