Speciation and Diversity Balance for Genetic Algorithms and Application to Structural Neural Network Learning

Foo, Y. W., Goh, C. and Li, Y. (2016) Speciation and Diversity Balance for Genetic Algorithms and Application to Structural Neural Network Learning. In: IJCNN 2016: IEEE World Congress on Computational Intelligence, Vancouver, Canada, 24-29 July 2016, pp. 1283-1290. (doi: 10.1109/IJCNN.2016.7727345)

Abstract

Following analyzing existing challenges in addressing the balance between exploration and exploitation encountered by evolutionary algorithms, this paper develops a Genetic Algorithm with speciation (GASP). It first incorporates a novel encoding scheme and recombination method for a balanced genetic divergence when locating global optima in complex applications, such as structural and dynamic design of an artificial neural network (NN). GASP also addresses the problem of defining a measure and track population diversity whose NN structure is subjected to continual reorganization during the evolution process. Further, a novel approach to the neural network phenotype is developed, which maps it to a distinct genome with a variable length capable of fully representing the multilayer feed-forward NN structure. Using the concept generalized from linguistic complexity, the distance between strings can thus be derived from the single string and substring counts. The GASP is then applied to an NN design problem to forecast the energy consumption of a built environment. With the optimal NN structure, diversity is tracked and improved. The results show that the GASP succeeds in obtaining excellent accuracy and speed.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Goh, Dr Cindy Sf and Li, Professor Yun
• Authors: Foo, Y. W., Goh, C., and Li, Y.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2161-4407
• Copyright Holders: Copyright © 2016 IEEE
• First Published: First published in 2016 International Joint Conference on Neural Networks (IJCNN) 2016: 1283-1290
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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