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- Title
Variable Chromosome Genetic Algorithm for Structure Learning in Neural Networks to Imitate Human Brain.
- Authors
Park, Kang-moon; Shin, Donghoon; Chi, Sung-do
- Abstract
Featured Application: Neural Network Structure Learning is expected to overcome difficulty of constructing a neural network structure. This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been designed on the basis of heuristic knowledge of an artificial intelligence (AI) expert. To overcome this limitation, in this study evolutionary approach (EA) has been utilized to automatically generate the proper artificial neural network (ANN) structures. VCGA has a new genetic operation called a chromosome attachment. By applying the VCGA, the initial ANN structures can be flexibly evolved toward the proper structure. The case study applied to the typical exclusive or (XOR) problem shows the feasibility of our methodology. Our approach is differentiated with others in that it uses a variable chromosome in the genetic algorithm. It makes a neural network structure vary naturally, both constructively and destructively. It has been shown that the XOR problem is successfully optimized using a VCGA with a chromosome attachment to learn the structure of neural networks. Research on the structure learning of more complex problems is the topic of our future research.
- Subjects
PLANT chromosomes; GENETIC algorithms
- Publication
Applied Sciences (2076-3417), 2019, Vol 9, Issue 15, p3176
- ISSN
2076-3417
- Publication type
Letter
- DOI
10.3390/app9153176