Abs t ract . This paper presents a machine-independent study of parallel genetic algorithm performance. Our approach utilizes a dataflow model of computat ion in conjunction with Sisal, an implicit parallel programming language. We compare problem-solving power and runt ime efficiency for several parallel genet ic algorithms under uniform conditions. The proposed method makes it possible to ide...

The problem of parallel and distributed function optimization with coevolutionary algorithms is considered. Two coevolutionary algorithms are used for this purpose and compared with sequential genetic algorithm (GA). The first coevolutionary algorithm called a loosely coupled genetic algorithm (LCGA) represents a competitive coevolutionary approach to problem solving and is compared with anothe...

A parallel genetic algorithm is presented to solve the well-known ca-pacitated lot-sizing problem. The approach is implemented on a massively parallel single instruction multiple data architecture with 16384 4-bit processors. Based on a random keys representation a schedule is backward oriented obtained which enables us to apply a very simple capacity check. Genetic algorithms are a general pur...

An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and real-valued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algori...

In this paper we present some results of our systematic studies of ne-grained parallel versions of the island model of genetic algorithms and of variants of the neighborhood model (also called diiusion model) on the massively parallel computer MasPar MP1 with 16k processing elements. These parallel genetic algorithms have been applied to a range of diierent problems (e.g. traveling salesperson,...

The traveling salesman problem (TSP) is to find a tour of a given number of cities (visiting each city exactly once and returning to the starting city) where the length of this tour is minimized. In this project, we implemented solutions to the traveling salesman problem using parallel genetic algorithm and simulated annealing, and compared the parallel performance and results of these two impl...

Linkage identification algorithms identify linkage groups — sets of loci tightly linked — before genetic optimizations for their recombination operators to work effectively and reliably. This paper proposes a parallel genetic algorithm (GA) based on the linkage identification algorithm and shows its effectiveness compared with other conventional parallel GAs such as master-slave and island mode...

This paper proposes a new crossover method for parallel distributed genetic algorithms (PDGAs). PDGAs with multiple subpopulations provide better solutions than conventional GAs with a single population. The proposed method, including the hybridization crossover and the best combinatorial crossover, is designed to increase the performance of PDGAs. The proposed method, which provides high local...