Genetic algorithm is very powerful technique to find approximate solution to search problems or patterns through application based on biological terms. Genetic algorithms use biologically inspired techniques such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover. The general strengths of genetic algorithms lie in their ability to explore t...

This paper describes a methodology for parallel theorem proving in a distributed environment, called deduction by Clause-Diffusion. This methodology utilizes parallelism at the search level, by having concurrent, asynchronous deductive processes searching in parallel the search space of the problem. The search space is partitioned among the processes by distributing the clauses and by subdividi...

A novel neural network model of pre-attention processing in visualsearch tasks is presented. Using displays of line orientations taken from Wolfe's experiments [1992], we study the hypothesis that the distinction between parallel versus serial processes arises from the availability of global information in the internal representations of the visual scene. The model operates in two phases. First...

Parallel Memetic Algorithms (PMAs) are a class of modern parallel meta-heuristics that combine evolutionary algorithms, local search, parallel and distributed computing technologies for global optimization. Recent studies on PMAs for large-scale complex combinatorial optimization problems have shown that they converge to high quality solutions significantly faster than canonical GAs and MAs. Ho...