The Twelfth IASTED International
Conference
on
Artificial Intelligence and Soft Computing
~ASC 2008~
September 1 – 3, 2008
Palma de Mallorca, Spain
TUTORIAL SESSION
Evolutionary Computation: A Comprehensive Methodology
Abstract
Evolutionary computing (EC) is a fast growing field of research especially in solving complex real life applications where an optimum solution is desirable; given a large number of conflicting constraints with usually one objective function. The tutorial is intended to give a framework about EC. It will include an exhaustive understanding of EC based techniques. Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) will be discussed in details. The session will start with a classical GA with an analogy to biological principles behind it. Various recent developments and suggestions at different stages of implementing GA will be discussed with illustrations. Similarly PSO will be introduced with an illustration. Few interesting applications of EC will be discussed. Finally, the framework will be used to discuss emerging and challenging issues of research in EC.
The tutorial will cover basics of EC, GA and PSO, before getting into their respective advanced versions; the attendees are assumed to have an average mathematical background only.
Biography of the Presenter
Amit Saxena is currently a Professor and Head, Department of Computer Science and Information Technology in G G University, Bilaspur – India; a reputed University of India. He was awarded Ph.D. in Computer Science in 1998. with title " Efficient Computation of DSP Problems Using Artificial Neural Network Techniques". He has several National and International publications in journals and conference proceedings to his credit. He has a number of invited talks, presentations in India and abroad. He has recently published a book in C Programming. He has worked in Faculty Positions in Malaysia (Multimedia University, Cyberjaya, Malaysia), Kuwait (Arab Open University). His area of research interest includes Data Mining, Soft Computing Techniques, Computational Intelligence, Genetic Algorithms, Incremental learning. .
