Evolutionary computation, offers practical advantages to the
researcher facing difficult optimization problems. These advantages
are multifold, including the simplicity of the approach, its robust
response to changing circumstance, its flexibility, and many other
facets. Many real-world search and optimization problems from sciences
and engineering are naturally posed as mathematical programming
problems involving multiple objectives. Due to the lack of suitable
techniques for finding multiple optimal solutions by classical means,
such problems are artificially converted into a single-objective
optimization problem
and solved. Unfortunately, the outcome of such methods is quite
dependent on the adopted conversion procedure.

Recently, evolutionary algorithms are proposed to solve these problems
in a less-subjective and efficient manner. Instead of finding one
solution at a time, evolutionary multiobjective optimization methods
find a number of Pareto-optimal solutions in one simulation and leave
the decision-making task for later.

In this tutorial, we introduce the fundamental concepts of
evolutionary algorithms and the basics of multi-objective optimization
with a quick review of the state-of-the-art techniques practiced in
this emerging field.  Some research pointers and some potential
application domains of EMO will also be highlighted in this tutorial.

References

Ajith Abraham, Lakhmi Jain and Robert Goldberg (Eds.), Evolutionary
Multiobjective Optimization: Theoretical Advances and Applications,
Springer Verlag, London, ISBN 1852337877, 12 Chapters, 315 pages,
2005.
http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-153-22-34527591-0,00.html

Crina Grosan, Multiobjective Adaptive Representation Evolutionary
Algorithm (MAREA) - A New Evolutionary Algorithm for Multiobjective
Optimization. In Proceedings of 9th World on-line Conference on Soft
Computing in Industrial Application, Applied Soft Computing
Technologies: The Challenge of Complexity, Advances in Soft Computing,
Springer Verlag, Germany, pp. 113-121, 2006.
 

BIO

Ajith Abraham currently works as a Distinguished Professor under the South Korean Government’s Institute of Information Technology Assessment (IITA) Professorship Program at Chung-Ang University, Korea. His primary research interests are in computational intelligence with a focus on using evolutionary computation techniques for designing intelligent paradigms. Application areas include several real world knowledge-mining applications like Web services, information security, bioinformatics, Web intelligence, energy management, financial modeling, weather analysis, fault monitoring, multi criteria decision-making etc. He has authored/co-authored over 200 research publications in peer reviewed reputed journals, book chapters and conference proceedings of which three have won ‘best paper’ awards.

He is serving the Editorial board of over a dozen International Journals and has also guest edited 12 special issues for reputed International Journals. Since 2001, he is actively involved in the Hybrid Intelligent Systems (HIS) and the Intelligent Systems Design and Applications (ISDA) series of International conferences. He was the General Chair of the 9th Online World Conference on Soft Computing in Industrial Applications (WSC9);  General Co-Chair of The Fourth IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST05), Japan and the Program Co-Chair of the Inaugural Conference on Next Generation Web Services Practices (NWeSP'05), Seoul, Korea. He received PhD degree from Monash University, Australia. More information at: http://www.softcomputing.net .