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Crossing genetic and swarm intelligence algorithms to generate logic circuits
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Orientador(es)
Resumo(s)
Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of
natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary
for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the
fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique
inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary
computation techniques such as GAs. The system is initialized with a population of random solutions and searches
for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and
mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current
optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization
between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied
to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best
features of each particular algorithm.
Descrição
Palavras-chave
Artificial intelligence Computational intelligence Evolutionary computation Genetic algorithms Particle swarm optimization Digital circuits
Contexto Educativo
Citação
Editora
World Scientific and Engineering Academy and Society (WSEAS)