This paper presents implementations of genetic algorithms in a tree shape parallel computer architecture. Diierent levels of parallelism involved in GAs are studied. In addition, basic models for parallel GAs are considered. The tree shape parallel computer system, GAPA (Genetic Algorithm Parallel Accelerator), is described in detail, including architecture and special hardware for GA computati...

A parallel two-level evolutionary algorithm which evolves genetic algorithms of maximum convergence velocity is presented. The meta-algorithm combines principles of evolution strategies and genetic algorithms in order to optimize continuous and discrete parameters of the genetic algorithms at the same time (mixed-integer optimization). The genetic algorithms which result from the meta-evolution...

Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]. Genetic algorithms (GAs) are developed as random search methods, which have not so sensitivity on primary data of the problems. They can be used in estimation of system parameters in order to obtain the best result. This can be achieved by optimization of an objective function. Genetic programm...

in this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. since cell formation problem is np-hard, two algorithms based on genetic and modified particle swarm optimization (mpso) algorithms are developed to solve the problem. for generating initial solutions in these algorithms, a new heuristic method is developed, which always cre...

In this paper we investigate the design of highly parallel Genetic Algorithms. The Traveling Salesman Problem is used as a case study to evaluate and compare di erent implementations. To x the various parameters of Genetic Algorithms to the case study considered, the Holland sequential Genetic Algorithm, which adopts di erent population replacement methods and crossover operators, has been impl...

Breeder Genetic Algorithms represent a class of random optimisation techniques gleaned from the science of population genetics, which have proved their ability to solve hard optimisation problems with continuous parameters. In this paper we test a parallel version of this technique against a sequential Breeder Genetic Algorithm on a typical inverse design problem in Aerodynamics, the problem of...

coverage improvement is one of the main problems in wireless sensor networks. given a finite number of sensors, improvement of the sensor deployment will provide sufficient sensor coverage and save cost of sensors for locating in grid points. for achieving good coverage, the sensors should be placed in adequate places. this paper uses the genetic and learning automata as intelligent methods for...

wireless sensor networks, due to the characteristics of sensors such as wireless communication channels, the lack of infrastructure and targeted threats, are very vulnerable to the various attacks. routing attacks on the networks, where a malicious node from sending data to the base station is perceived. in this article, a method that can be used to transfer the data securely to prevent attacks...

various synthesis methods have been proposed in the literature for reversible and quantum logic circuits. however, there are few algorithms to optimize an existing circuit with multiple constraints simultaneously. in this paper, some heuristics in genetic algorithms (ga) to optimize a given circuit in terms of quantum cost, number of gates, location of garbage outputs, and delay, are proposed. ...

the finite element method (fem) is one of advanced methods in modeling of solid mechanics used in the design and analyzing of prosthesis in the human body. one of the important factors in designing a femur (the linkage between basin and knee) which is under complicated stresses is to minimize the amount of the stress in the components of the prosthesis due to the body weight. in this research t...