Genetic algorithms are a population-based Meta heuristics. They have been successfully applied to many optimization problems. However, premature convergence is an inherent characteristic of such classical genetic algorithms that makes them incapable of searching numerous solutions of the problem domain. A memetic algorithm is an extension of the traditional genetic algorithm. It uses a local se...

Premature Convergence and genetic drift are the inherent characteristics of genetic algorithms that make them incapable of finding global optimal solution. A memetic algorithm is an extension of genetic algorithm that incorporates the local search techniques within genetic operations so as to prevent the premature convergence and improve performance in case of NP-hard problems. This paper propo...

An improved genetic algorithm is presented to select optimal web services composite plans from a lot of composite plans on the basis of global Quality-of-Service (QoS) constraints. The relation matrix coding scheme of genome is its basis. In this genetic algorithm, an especial fitness function and a mutation policy are proposed on the basis of the relation matrix coding scheme of genome. They e...

Genetic Algorithms have gained wide popularity due to their conceptual simplicity and their ability to obtain approximate solutions for complex searching and optimization problems. Their wide applicability contrasts with their rather undeveloped theoretical basis. In particular, the convergence of genetic algorithms has been one of the most challenging theoretical issues in the evolutionary com...

A hybrid genetic scheduling strategy (H-GA) is described in this article, H-GA combines with grouping and load balancing strategy based on traditional genetic algorithm (GA). First, tasks are divided into several different subgroups by task granularity. Then, task subgroup which is selected by granularity from big to small is used to schedule by the genetic algorithm, and during scheduling, the...

Radio communication speed has improved by leaps and bounds with developments in communication technology. In a wireless LAN, clients connect to a network to communicate with access points. Various approaches have benn used over the years to maximize the efficiency of access point configurations, such as the simplex method, Tabu search, and the genetic algorithm. This paper describes how genetic...

⎯The convergence of genetic algorithm is mainly determined by its core operation crossover operation. When the objective function is a multiple hump function, traditional genetic algorithms are easily trapped into local optimum, which is called premature convergence. In this paper, we propose a new genetic algorithm with improved arithmetic crossover operation based on gradient method. This cro...

In this paper we obtain bounds on the probability of convergence to the optimal solution for the compact genetic algorithm (cGA) and the population based incremental learning (PBIL). Moreover, we give a sufficient condition for convergence of these algorithms to the optimal solution and compute a range of possible values for algorithm parameters at which there is convergence to the optimal solu...