Adopting gene expression programming to generate extension strategies for incompatible problem

Adopting gene expression programming to generate extension strategies for incompatible problem Exploring solving strategies for incompatible problem is a challenging task. Although extension strategy generating method (ESGM) can address incompatible problem by expanding reasoning and extension transformations, the solving process often suffers a combination explosion of computational cost. In order to overcome this shortcoming, a new approach to performing extension transformations based on gene expression programming (GEP) is proposed. The method is able to establish superior operations of extension transformations heuristically and iteratively, which avoids the combination explosion effectively. In order to make GEP adapt to such applying requirement, chromosome architecture, decoding mode, individual selection and convergence criteria are restudied. The proposed method is illustrated with the application of ESGM to a self-guided touring route design problem. Numerical results verify that the proposed method helps provide extension strategies efficiently and has a huge potential for more complex incompatible problem solving. Neural Computing and Applications Springer Journals

Adopting gene expression programming to generate extension strategies for incompatible problem

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Springer London
Copyright © 2016 by The Natural Computing Applications Forum
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
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