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Evolutionary Multi‐Objective Optimization (EMO)

Evolutionary Multi­‐Objective Optimization (EMO)

Kalyanmoy Deb
Gurmukh and Veena Mehta Chair Professor
Department of Mechanical Engineering
Indian Institute of Technology Kanpur, India
E-mail: deb@iitk.ac.in


Many practical problem‐solving tasks are naturally posed as search and optimization problems having multiple conflicting goals. These problems give rise to a set of trade-off Pareto‐optimal solutions that must be found and then analyzed to choose a single preferred solution. Since the outcome of the optimization task is a set of solutions, evolutionary algorithms were considered to be ideal candidates for multi‐criterion optimization. Initial algorithms, dated back to early nineties, used the mathematical partial ordering concept and combined with evolutionary niche‐preserving techniques. The success of initial algorithms gave birth to the field of evolutionary multi‐criterion optimization (EMO) which is now well‐established with dedicated conferences and workshops, taught as a semester­‐long course with dedicated textbooks, practiced in industries with the help of a number of dedicated commercial softwares, and nurtured and developed by dedicated full‐time researchers.

In this two‐hour long tutorial, we shall preview the evolution of key developments in the field of EMO since its inception in early nineties, review the basic algorithms, and present some key applications. Thereafter, we shall discuss recent research challenges that are driving the EMO researchers and practitioners. EMO algorithms are still not efficient in handling four or more objectives. We shall preview the difficulties and present some viable directions for handling so­‐called "many‐objective" optimization problems. Finding a set of trade‐off solutions is half the story, choosing a single preferred solution is the other half of the story, which is far from being trivial. We shall review the past preference‐based EMO strategies and discuss some recent EMO­‐based interactive methodologies for this purpose. The principle of EMO can ideally suit to solve other search and optimization problems that are not originally multi‐objective. We shall demonstrate a number of "multi-­objectivized" case studies that should motivate its application to other problems. In addition, we shall discuss some key advancement of EMO in handling more practicalities, such as non‐linear constraints, uncertainties in decision variables and parameters, computationally expensive problems, and dynamic problems. Finally, the recently proposed "innovization" task of using EMO to decipher useful knowledge in practical problems will be discussed and demonstrated.



Kalyanmoy Deb is an Endowed Chair Professor of Mechanical Engineering at Indian Institute of Technology Kanpur in India. He is also an Adjunct Professor at the Department of Information and Service Economy in the Aalto University in Finland and a visiting Professor at the University of Skovde in Sweden. Prof Deb's main research interests are in evolutionary optimization algorithms and their application in optimization and machine learning. He is largely known for his seminal research in developing and applying Evolutionary Multi‐Criterion Optimization (EMO). He has led and has been very active in getting the EMO and MCDM fields together through joint conference organizations, book writing, executing collaborative research projects.

For his pioneering research in multi‐criterion optimization and decision‐making, he was awarded the prestigious 'Infosys Prize', `TWAS Prize’, 'Cajastur Mamdani Prize' and 'J. C. Bose Fellowship' in 2011, 'Edgeworth-­‐Pareto' award in 2008 by Intl. Society of MCDM. In India, he received the prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences for the year 2005. He has also received the `Thomson Citation Laureate Award' from Thompson Scientific for having the highest number of citations in Computer Science during the past ten years in India. His 2002 IEEE‐TEC NSGA‐II paper is now judged as the Most Highly Cited paper and a Current Classic by Thomson Reuters having more than 3,300 citations.

He is a fellow of Indian National Science Academy (INSA), Indian National Academy of Engineering (INAE), Indian Academy of Sciences (IASc), and International Society of Genetic and Evolutionary Computation (ISGEC). He has received Friedrich Wilhelm Bessel Research award from Alexander von Humboldt Foundation in 2003. He has written two text­‐books on optimization, 11 edited books, and more than 330 international journal and conference research papers. He is associate editor and in the editorial board on 18 major international journals.

More information about his research can be found from http://www.iitk.ac.in/kangal/deb.htm.


The length of the tutorial:
two hours.


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