Abstract
Evolutionary computation techniques need to interact with a fitness function or an objective function for the selection to be made properly. In cases objective functions are not well defined, evolutionary algorithms may not be able to perform properly. In this paper, we propose to use a well-trained neural network model to provide estimates of objective values at points that we have not experienced to support the selection process of the evolutionary algorithm. An example of gasoline blending task is used to demonstrate that such an integrated system functions as a powerful tool for product design.
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© 1995 Springer-Verlag/Wien
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Yip, P.P.C., Pao, YH. (1995). A Perfect Integration of Neural Networks and Evolutionary Algorithms. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_25
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_25
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive