Adaptive Multi-Objective Evolutionary Algorithms for Overtime Planning in Software Projects

Self-Adaptive Mechanism for Multi-objective Evolutionary Algorithms

Evolutionary algorithms can efficiently solve multi-objective optimization problems (MOPs) by obtaining diverse and near-optimal solution sets. However, the performance of multi-objective evolutionary algorithms (MOEAs) is often limited by the suitability of their corresponding parameter settings with respect to different optimization problems. The tuning of the parameters is a crucial task whi...

Adaptive and Assortative Mating Scheme for Evolutionary Multi-Objective Algorithms

We are interested in the role of restricted mating schemes in the context of evolutionary multi-objective algorithms. In this paper, we propose an adaptive assortative mating scheme that uses similarity in the decision space (genotypic assortative mating) and adapts the mating pressure as the search progresses. We show that this mechanism improves the performance of the simple evolutionary algo...

Multi-Objective Evolutionary Algorithms

Evolutionary Multi-Objective Algorithms

The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively simple implementation; besides they have the ability to reflect the philosophy of evolutionary co...

Multi-Objective Evolutionary Algorithms

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to get...