Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems

Arijit Bhattacharya, Ajith Abraham, Crina Grosan, Pandian Vasant, Sangyong Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting flexible manufacturing systems (FMS) from a group of candidate FMS's. First, multi-criteria decision-making (MCDM) methodology using an improved S-shaped membership function has been developed for finding out the 'best candidate FMS alternative' from a set of candidate-FMSs. The MCDM model trade-offs among various parameters, namely, design parameters, economic considerations, etc., affecting the FMS selection process in multi-criteria decision-making environment. Genetic algorithm is used to evolve the architecture and weights of the proposed neural network method. Further, a back-propagation (BP) algorithm is used as the local search algorithm. The selection of FMS is made according to the error output of the results found from the MCDM model.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2006
Subtitle of host publicationThird International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III
PublisherSpringer-Verlag Berlin Heidelberg
Number of pages7
ISBN (Print)3540344829, 9783540344827
Publication statusPublished - 1 Jan 2006
Externally publishedYes
Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China
Duration: 28 May 20061 Jun 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3973 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

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