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Incorporation family competition into Gaussian and Cauchy mutations to training neural networks using an evolutionary algorithm
Jinn Moon Yang
,
Jorng Tzong Horng
, Cheng Yen Kao
資訊工程學系
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Keyphrases
Adaptation Rules
100%
Adaptive mutation
50%
Algorithm Evolution
50%
Artificial Ant Problem
50%
Boolean Functions
50%
Cauchy mutation
100%
Competition Principle
50%
Convergence Rate
50%
Evolution Strategy
50%
Evolutionary Algorithms
100%
Evolutionary Method
50%
Evolutionary Programming
50%
Gaussian mutation
100%
Genetic Algorithm
50%
Global Strategy
50%
Learning Behavior
50%
Local Strategies
50%
Mutation Operator
50%
Neural Network
50%
Neural Network Training
100%
Solution Convergence
50%
Solution Quality
50%
Task Domain
50%
Engineering
Boolean Function
100%
Convergence Speed
100%
Experimental Result
100%
Gaussians
100%
Genetic Algorithm
100%
Mutation Operator
100%
Neural Network Training
100%
Computer Science
Boolean Function
50%
Convergence Speed
50%
Evolution Strategy
50%
Evolutionary Algorithm
100%
Evolutionary Programming
50%
Experimental Result
50%
Genetic Algorithm
50%
Mutation Operator
50%
Neural Network
50%
Neural Network Training
100%
Solution Quality
50%
Mathematics
Boolean Function
50%
Evolutionary Algorithms
100%
Gaussian Distribution
100%
Genetic Algorithm
50%
Neural Network
100%