This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms including parallel island models and parallel cellular genetic algorithms The tutorial also illustrates genetic search by hyperplane sampling The theoretical foundations of genetic algorithms are reviewed include the schema theorem as well as recently developed exact models of the canon...

Simple Genetic Algorithms are used to solve optimization problems. Genetic Algorithm also comes with a parallel implementation as Parallel Genetic Algorithm (PGA). PGA can be used to reduce the execution time of SGA and also to solve larger size instances of problems. In this paper, different implementations for PGA have been discussed with their frameworks. In this implementation, all PGA are ...

In parallel processing systems, a fundamental consideration is the maximization of system performance through task mapping. A good allocation strategy may improve resource utilization and increase signi cantly the throughput of the system. We demonstrate how to map the tasks among the processors to meet performance criteria, such as minimizing execution time or communication delays. We review t...

Parallelization of genetic algorithms (GAs) has received considerable attention in recent years. The reason for this is the availability of suitable computational resources and the need for solving harder problems in reasonable time. We describe a new parallel self-adaptive GA for solving the data clustering problem. The algorithm utilizes island parallelization implemented using genebank model...

Tasks scheduling is the most challenging problem in the parallel computing. Hence, the inappropriate scheduling will reduce or even abort the utilization of the true potential of the parallelization. Genetic algorithm (GA) has been successfully applied to solve the scheduling problem. The fitness evaluation is the most time consuming GA operation for the CPU time, which affect the GA performanc...

multiple traveling salesman problem (mtsp) is one of the most popular operation research problem and is known as combinatorial optimization problems. mtsp is an extension version of the famous and widely used problem named traveling salesman problem (tsp). because of its benefices in various domains, many researchers have tried to solve that, and many methods have proposed so far. mtsp is a np-...

In design of a parallel resonant induction heating system, choosing a proper capacitancefor the resonant circuit is quite important. The capacitance affects the resonant frequency, outputpower, Q-factor, heating efficiency and power factor. In this paper, the role of equivalent seriesresistance (ESR) in the choice of capacitance is significantly recognized. Optimal value of resonancecapacitor i...

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the c...

The parallel genetic algorithm (PGA) is a prototype of a new kind of a distributed algorithm. It is based on a parallel search by individuals all of which have the complete problem description. The information exchange between the individuals is done by simulating biological principles of evolution. The PGA is totally asynchronous, running with maximal eeciency on MIMD parallel computers. The s...

Parallel genetic algorithms (PGA) use two major modiications compared to the genetic algorithm. Firstly, selection for mating is distributed. Individuals live i n a 2-D w orld. Selection of a mate is done by e a c h individual independently in its neighborhood. Secondly, each individual may improve its tness during its lifetime by e.g. local hill-climbing. The PGA is totally asynchronous, runni...