The genetic algorithm has been widely used in many fields as an easy robust global search and optimization method. This paper introduces a new approach to find fuzzy rules using niche genetic algorithms with elitist migrating operator. It uses binary coding scheme. In order to make search results stable, elitist migrating operator reserves good individuals into an elitist sub-population. The si...

An elitist selection adaptive Genetic Algorithm is presented to select an optimal cloud service composite plan from a lot of composite plans on the basis of global Qualityof-Service (QoS) constraints. In this Algorithm, some strategies are provided to improve convergence and population diversity and raise fitness result. These strategies include an elitist selection strategy, an individual sele...

This paper introduces evolutionary algorithms with its applications in multi-objective optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed with their advantages and disadvantages. We also discuss constrained multiobjective evolutionary algorithms and their applications in various areas.

| This paper oers suucient conditions to prove global convergence of non{elitist evolutionary algorithms. If these conditions can be applied they yield bounds of the convergence rate as a by{product. This is demonstrated by an example that can be calculated exactly. KeyWords| global convergence, non{elitist evolutionary algorithm , martingale theory

In this paper, a non-dominated sorting based multi objective EA (MOEA), called Elitist non dominated sorting genetic algorithm (Elitist NSGA) has been presented for solving the fault section estimation problem in automated distribution systems, which alleviates the difficulties associated with conventional techniques of fault section estimation. Due to the presence of various conflicting object...