The Harmony Search Algorithm for Supervised Training of Neural Network

Ali Kattan

ISBN 10: 6139472555 / ISBN 13: 9786139472550

Published by LAP LAMBERT Academic Publishing Apr 2019, 2019

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 260 pp. Englisch.

Seller Inventory # 9786139472550

Bibliographic Details

• Title: The Harmony Search Algorithm for Supervised Training of Neural Network
• Publisher: LAP LAMBERT Academic Publishing Apr 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 6139472555
• ISBN 13: 9786139472550
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 17D millimeters 220 millimeters width by 150 millimeters height by 17 millimeters depth
• Weight: 405 grams

About this title

Synopsis

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.