EVOLUTION OF NEURAL NETWORK AND POLYNOMIAL NETWORK - Knowledge Mining Using Intelligent Agents - page 61

Chapter 3
EVOLUTION OF NEURAL NETWORK AND
POLYNOMIAL NETWORK
B. B. MISRA ∗ ,P.K.DASH † and G. PANDA ‡
∗ Department of Information Technology,
Silicon Institute of Technology,
Bhubaneswar-751024, Orissa, India
[e-mail]
† Multidisciplinary Research Cell,
SOA University,
Bhubaneswar-751030, Orissa, India
pkdash [e-mail]
‡ School of Electrical Sciences,
Indian Institute of Technology,
Bhubaneswar, Orissa, India
[e-mail]
Natural evolution is the process of optimizing the characteristics and
architecture of the living beings on earth. Possibly evolving the optimal
characteristics and architectures of the living beings are the most complex
problems being optimized on earth since time immemorial. The evolutionary
technique, though seems to be very slow, is one of the most powerful tools
for optimization, especially when all the existing traditional techniques fail.
This chapter presents how these evolutionary techniques can be used to
generate optimal architecture and characteristics of different machine learning
techniques. Mainly two different types of networks considered in this chapter
for evolution are Artificial Neural Network and Polynomial Network. Though
research has been conducted on evolution of Artificial Neural Network,
research on evolution of polynomial Networks is still in its early stage.
Evolution of both the networks have been discussed in detail. Experimental
results are presented for further clarification of the evolution process of such
networks.
3.1. Introduction
Evolutionary computation (EC) involves the study of the computational
techniques based on the principles of natural evolution. Evolution is
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