The availability of faster and cheaper parallel computers makes it possible to apply genetic algorithms to large populations and very complex applications. This report presents a survey of current implementation techniques for genetic algorithms on

This paper examines the effects of relaxed synchronization on both the numerical and parallel efficiency of parallel genetic algorithms (GAs). We describe a coarse-grain geographically structured parallel genetic algorithm. Our experiments provide preliminary evidence that asynchronous versions of these algorithms have a lower run time than synchronous GAs. Our analysis shows that this improvem...

In this paper, the flow-shop scheduling problem with unrelated parallel machines at each stage as well as sequence-dependent setup times under minimization of the sum of earliness and tardiness are studied. The processing times, setup times and due-dates are known in advance. To solve the problem, we introduce a hybrid memetic algorithm as well as a particle swarm optimization algorithm combine...

We design sequential and parallel genetic algorithms, simulated annealing algorithms and improved greedy algorithms for the shortest common superstring problem(SCS), which is to find the shortest string that contains all strings from a given set of strings. The SCS problem is NP-complete [7]. It is even MAX SNP hard [2] i.e. no polynomial-time algorithm exists, that can approximate the optimum ...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...

This paper proposes a new algorithm of a simulated annealing (SA): Parallel Simulated Annealing using Genetic Crossover (PSA/GAc). The proposed algorithm consists of several processes, and in each process SA is operated. The genetic crossover is used to exchange information between solutions at fixed intervals. While SA requires high computational costs, particularly in continuous problems, thi...

Data-intensive Computing has played a key role in processing vast volumes of data exploiting massive parallelism. Parallel computing frameworks have proven that terabytes of data can be routinely processed. Mapreduce is a parallel programming model and associated implementation founded by Google, which is one of the leading companies in IT. Genetic Algorithms have increasingly applied on parall...

Genetic algorithms (GAs) pose several problems. Probably, the most important one is that the search ability of ordinary GAs is not always optimal in the early and final stages of the search because of fixed GA parameters. To solve this problem, we proposed the fuzzy adaptive search method for genetic algorithms (FASGA) that is able to tune the genetic parameters according to the search stage by...