Genetic Algorithm Particle Swarm Optimization based Hardware Evolution Strategy

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Provided by: WSEAS
Topic: Hardware
Format: PDF
There are many problems exist in the Evolutionary Algorithm (EA) using Genetic Algorithm (GA), such as slow convergence speed, being easy to fall into the partial optimum, etc. Particle Swarm Optimization (PSO) can accelerate the space searching and reduce the number of convergences and iterations. The proposed characteristics of Genetic Algorithm Particle Swarm Optimization (GAPSO) are proved by many examples, when the GA, PSO and GAPSO are adopted under the same conditions, GAPSO can get the least iteration numbers and the highest evolvable success rate.
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