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The Game of Wall Cops and Robbers Read chapter Read first chapter

2016 | OriginalPaper | Chapter

An Elitist Genetic Algorithm Based Extreme Learning Machine

Authors : Vimala Alexander, Pethalakshmi Annamalai

Published in: Computational Intelligence, Cyber Security and Computational Models

Publisher: Springer Singapore

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Abstract

Extreme Learning Machine (ELM) has been proved to be exceptionally fast and achieves more generalized performance for learning Single-hidden Layer Feedforward Neural networks (SLFN). In this paper, a Genetic Algorithm (GA) is proposed to choose the appropriate initial weights, biases and the number of hidden neurons which minimizes the classification error. The proposed GA incorporates a novel elitism approach to avoid local optimum and also speed up GA to satisfy the multi-modal function. The experimental results indicate the superior performance of the proposed algorithm with lower classification error.

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Metadata
Title
An Elitist Genetic Algorithm Based Extreme Learning Machine
Authors
Vimala Alexander
Pethalakshmi Annamalai
Copyright Year
2016
Publisher
Springer Singapore
Book
Computational Intelligence, Cyber Security and Computational Models

Print ISBN: 978-981-10-0250-2

Electronic ISBN: 978-981-10-0251-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-981-10-0251-9_29

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