Quantum-inspired Evolutionary Algorithms and Binary Particle Swarm Optimization for Training MLP and SRN Neural Networks
Abstract
This paper presents a comparison of two machine learning methods inspired by nano-scale and macro-scale natural processes and related to distributed intelligence, namely Quantum—Inspired Evolutionary Algorithm (QEA) and Binary Particle Swarm Optimization (BPSO). QEA is based on the concepts and principles of Quantum Computing, such as a quantum bit (Q-bit) and superposition of states. QEA uses a Q-bit for the probabilistic representation and a Q-bit individual as a string of Q-bits. A modified QEA with less memory requirements is also presented. The effectiveness of these algorithms in binary search space are compared for training neural networks. Results are presented for Multilayer Perceptrons (MLPs) and Simultaneous Recurrent Neural Networks (SRNs). For neural networks trained on complex nonlinear functions, the QEA based algorithms achieve convergence faster than BPSO.
Recommended Citation
G. K. Venayagamoorthy and G. Singhal, "Quantum-inspired Evolutionary Algorithms and Binary Particle Swarm Optimization for Training MLP and SRN Neural Networks," Journal of Computational and Theoretical Nanoscience, American Scientific Publishers, Dec 2005.
Department(s)
Electrical and Computer Engineering
Sponsor(s)
National Science Foundation (U.S.)
Keywords and Phrases
Binary Training Algorithms; Multilayer Perceptron; Particle Swarm Optimization; Quantum Inspired Evolutionary Algorithms; Simultaneous Recurrent Neural Network
International Standard Serial Number (ISSN)
1546-1955
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2005 American Scientific Publishers, All rights reserved.
Publication Date
01 Dec 2005
