A Perfect Integration of Neural Networks and Evolutionary Algorithms

  • Percy P. C. Yip
  • Yoh-Han Pao
Conference paper


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.


Evolutionary Algorithm Input Ingredient Evolutionary Computation Technique Octane Rate Neural Network Module 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • Percy P. C. Yip
    • 1
    • 2
  • Yoh-Han Pao
    • 1
    • 2
  1. 1.Case Western Reserve UniversityUSA
  2. 2.AI Ware, Inc.USA

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