Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/4307
Título: Crossing genetic and swarm intelligence algorithms to generate logic circuits
Autor: Reis, Cecília
Machado, J. A. Tenreiro
Palavras-chave: Artificial intelligence
Computational intelligence
Evolutionary computation
Genetic algorithms
Particle swarm optimization
Digital circuits
Data: 2009
Editora: World Scientific and Engineering Academy and Society (WSEAS)
Relatório da Série N.º: WSEAS Transactions on Computers; Vol. 8, Issue 9
Resumo: 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.
Peer review: yes
URI: http://hdl.handle.net/10400.22/4307
ISSN: 1109-2750
Versão do Editor: http://www.wseas.us/e-library/transactions/computers/2009/29-631.pdf
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