Minimization of Binary Decision Diagrams by Adaptive Hierarchy Genetic Algorithm and Its Application in Circuit Test Generation

Zhang, Pan; Chen, Ling

doi:10.1007/978-3-642-31528-2_29

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…O método de controle consiste em utilizar a probabilidade Bayesiana para ajustar cada parâmetro com base em seu desempenho (TC-RS:2). Zhongliang (2013) [102] apresenta um método de controle para o tamanho da população e a probabilidade da mutação. O Tutorial Sobre o Uso de Técnicas para Controle Online de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva tamanho da população é ajustado com uma função simples com base no fitness da população.…”

Section: Controle De Parâmetros Em Algoritmos Bio-inspiradosunclassified

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

André

Parpinelli

2014

RITA

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Nature has always been a great source of inspiration for the development of computational approaches for optimization. Two major groups representing this class of biologically inspired algorithms are Swarm Intelligence and Evolutionary Computation. Such algorithms are called metaheuristics and are recognized to be efficient approaches for solving complex problems. Both Swarm Intelligence and Evolutionary Computation share common features such as the use of stochastic components during the optimization process and various parameters for configuration. The setup of parameters of an algorithm has an important role in defining its behavior, guiding the search and biasing the quality of the solutions found. However, adjusting the parameters is not a simple task, becoming an optimization problem within the problem being optimized. In addition, an appropriate setting for the parameters may change during the optimization process making this task even harder. There are two ways to adjust the parameters of an algorithm. The offline control that is performed before running the algorithm and parameter values remains fixed and the online control where parameter values may change during the optimization process. This article focuses on reviewing the online parameter control strategies applied in Evolutionary Computation and Swarm Intelligence. As a result, this review analyzes and points out the key techniques and algorithms used and suggests some directions for future research.

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Section: Controle De Parâmetros Em Algoritmos Bio-inspiradosunclassified

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

André

Parpinelli

2014

RITA

View full text Add to dashboard Cite

show abstract

Minimization of Binary Decision Diagrams by Adaptive Hierarchy Genetic Algorithm and Its Application in Circuit Test Generation

Cited by 1 publication

References 8 publications

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

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