Abstract
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.
Highlights
The purpose of artificial intelligence (AI) is to imitate the human intelligence [1]
The Artificial neural network (ANN) is already widely used for an estimation of automotive brake pressure, which has a great potential to achieve a simple design of the braking control system without sensors [3]
This study proposed a chromosome attachment as a new genetic operation to implement the structure learning of ANN
Summary
The purpose of artificial intelligence (AI) is to imitate the human intelligence [1]. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand [8] For these reasons, various studies have been carried out to generate the network architecture automatically [11,12]. It makes the ANN vary both constructively and destructively Through this operation, a structure learning method in the process of a neural architecture search (NAS) is possible. GA has been the most common method used for designing neural network architecture [11,12,13,14,15,16,17,18] In these studies, GA was used to generate the network structures depending on their purposes. Variable chromosome genetic algorithm (VCGA) is applied to this method to implement the structure learning of ANN so as to imitate a human brain.
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