978-613-9-47255-0

The Harmony Search Algorithm for Supervised Training of Neural Network

Design & Implementation

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Summary:

Within the field of Artificial Intelligence, there are basically two paradigms for the supervised training of Feed-forward Artificial Neural Network (FFANN): the trajectory-driven paradigm, such as Backpropagation, and the evolutionary Stochastic Global Optimization paradigm (SGO), such as Genetic Algorithm. One of the relatively young SGO methods is the Harmony Search (HS) algorithm, which draws its inspiration not from biological or physical processes but from the improvisation process of Jazz musicians. HS was reported to be competitive alternative to other SGO methods. It has been used successfully in many applications mostly in engineering and industry. In this work the HS algorithm is adapted for the supervised training of FFANN and the performance is evaluated using different benchmarking problems. Two enhancements are introduced to achieve better convergence condition and better performance. A parallel implementation is also included along with performance analysis.

Author:

Ali Kattan

Biographie:

Ali Kattan, a member of IEEE since 2009, is a PhD holder and an Assistant Professor of Computer Sciences. His research interests include machine learning, optimization, robotics, IoT and web programming. He is currently a staff member of the American Stratford University located in the city of Erbil (North of Iraq).

Number of Pages:

260

Book language:

English

Published On:

2019-04-01

ISBN:

978-613-9-47255-0

Publishing House:

LAP LAMBERT Academic Publishing

Keywords:

Neural Network, training, Harmony Search, Algorithm, Artificial Intelligence

Product category:

COMPUTERS / General