This paper proposed a novel distributed memetic evolutionary model, where four modules exploration, intensified exploitation, knowledge transfer, and restart are coevolved to maximize their strengths achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation an external elite archive in parallel with bala...

One of the fastest growing areas of evolutionary algorithm research is the enhancement of genetic algorithms by combination with local search methods or memes: often known otherwise as memetic algorithms. However there is often little theoretical basis on which to characterize the choice of memes that lead to successful memetic algorithm performance. In this paper, we investigate empirically th...

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...

Memetic algorithm(MA) is a population based heuristic search approach for optimization problems similar to genetic algorithm. GAs, however, rely on the concept of biological evolution, but MAs, in contrast, and mimic cultural evolution. The cryptanalysis of simplified data encryption standard can be formulated as NP-Hard combinatorial problem. In this paper, a comparison between memetic algorit...

We present in this paper a new multiobjective memetic algorithm scheme called MEMOX. In current multiobjective memetic algorithms, the parents used for recombination are randomly selected. We improve this approach by using a dynamic hypergrid which allows to select a parent located in a region of minimal density. The second parent selected is a solution close, in the objective space, to the fir...

The cryptanalysis of simplified data encryption standard can be formulated as NP-Hard combinatorial problem. The goal of this paper is two fold. First we want to make a study about how evolutionary computation techniques can efficiently solve the NP-Hard combinatorial problem. For achieving this goal we test several evolutionary computation techniques like memetic algorithm, genetic algorithm a...

in this article, a multi-objective memetic algorithm (ma) for rule learning is proposed. prediction accuracy and interpretation are two measures that conflict with each other. in this approach, we consider accuracy and interpretation of rules sets. additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. this...

In this paper we regard a break scheduling problem originating in the area of supervision personnel. This complex task requires the assignment of several breaks per shift, satisfying various constraints reflecting labour rules, staffing requirements and ergonomic criteria. To solve this problem we propose a memetic algorithm combining a genetic algorithm and a local search procedure. We evaluat...

The incorporation of local search operators into a genetic algorithm has provided very good results in certain scheduling problems. The resulting algorithm from this hybrid approach has been termed a Memetic Algorithm. This paper investigates the use of a memetic algorithm for the thermal generator maintenance scheduling problem. The local search operators alone have been found (in earlier work...

In our previous work we proposed some extensions of the Levenberg-Marquardt algorithm; the Bacterial Memetic Algorithm and the Bacterial Memetic Algorithm with Modified Operator Execution Order for fuzzy rule base extraction from inputoutput data. Furthermore, we have investigated fuzzy flip-flop based feedforward neural networks. In this paper we introduce the adaptation of the Bacterial Memet...