Niching methods extend genetic algorithms to domains that require the location of multiple solutions. This study examines and compares four niching methods | sharing, crowding, sequential niching, and parallel hillclimbing. It focuses on the diierences between parallel and sequential niching. The niching methods undergo rigorous testing on optimization and classiication problems of increasing d...

Considering the increasingly high attention to quality, promoting the reliability of products during designing process has gained significant importance. In this study, we consider one of the current models of the reliability science and propose a non-linear programming model for redundancy allocation in the series-parallel systems according to the redundancy strategy and considering the assump...

Parallel algorithms based on stochastic hill-climbing and parallel algorithms based on simple elements of a genetic algorithm for the one-sided bipartite crossing number problem, used in row-based vlsi layout, were investigated. These algorithms were run on a pvm cluster. The experiments show that the parallel approach does not bring faster computation but it does, however, much more importantl...

Parallel and multiprocessing algorithms break down significant numerical problems into smaller subtasks, reducing the total computing time on multiprocessor multicore computers. programming is well supported in proven languages such as C Python, which are suited to “heavy-duty” computational tasks. Historically, Python has been regarded a strong supporter of parallel due global interpreter lock...

This paper introduces and analyzes a parallel method of simulated annealing. Borrowing from genetic algorithms, an eeective combination of simulated annealing and genetic algorithms, called parallel recombinative simulated annealing, is developed. This new algorithm strives to retain the desirable asymptotic convergence properties of simulated annealing, while adding the populations approach an...

Parallel processing has been a valuable tool for improving the performance of many algorithms. Solving intractable problems is an attractive application of parallel processing. Traditionally, exhaustive search techniques have been used to nd solutions to NP-complete problems. However, the performance beneet of paralleliza-tion of exhaustive search algorithms can only provide linear speedup, whi...

Algorithmic skeleton has received attention as an efficient method of parallel programming in recent years. Using the method, the programmer can implement parallel programs easily. In this study, a set of efficient algorithmic skeletons is introduced for use in implementing parallel genetic algorithm (PGA).A performance modelis derived for each skeleton that makes the comparison of skeletons po...

In this paper, the MPI master-slave parallel genetic algorithm is implemented by analyzing the basic genetic algorithm and parallel MPI program, and building a Linux cluster. This algorithm is used for the test of maximum value problems (Rosen brocks function) .And we acquire the factors influencing the master-slave parallel genetic algorithm by deriving from the analysis of test data. The expe...

Genetic Algorithms (GAs) are powerful search techniques that are used to solve difficult problems in many disciplines. Unfortunately, they can be very demanding in terms of computation load and memory. Parallel Genetic Algorithms (PGAs) are parallel implementations of GAs which can provide considerable gains in terms of performance and scalability. PGAs can easily be implemented on networks of ...