In this paper, we describe a multi-objective evolutionary algorithm, that uses clustering selection and does not need any additional parameter like others. It clusters the population into a exible number of clusters employing x-means from [Pelleg and Moore, 2000]. First, the selective tness is assigned to clusters and in second place to individuals of clusters. We show three hybrid variants inc...

Evolutionary Algorithms are recognized to be efficient to deal withMulti-objective Optimization Problems(MOPs) which are difficult to be solved with traditional methods. Here a newMulti-objective Optimization Evolutionary Algorithm named DGPS which is compound with Geometrical Pareto Selection Method (GPS), Weighted SumMethod (WSM) and Dynamical Evolutionary Algorithm (DEA) is proposed. Some fa...

A hybrid unsupervised learning algorithm, which is termed as Parallel Rough-based Archived Multi-Objective Simulated Annealing (PARAMOSA), is proposed in this article. It comprises a judicious integration of the principles of the rough sets theory and the scalable distributed paradigm with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of...

Sequential decision-making problems with multiple objectives are known as multi-objective reinforcement learning. In these scenarios, decision-makers require a complete Pareto front that consists of optimal solutions. Such enables to understand the relationship between and make informed decisions from broad range However, existing methods may be unable search for solutions in concave regions or...