IEEE CIS Task Force on Multi-Objective Evolutionary Algorithms

Active Members of the Task Force Group (to be completed)


Why a Tasking Force Group?

The main aim of the task force groups of the IEEE CIS Technical Committee on Evolutionary Computation is to promote the research in evolutionary multi-objective Optimization (EMO). EMO is already an established field and there are many conferences, workshops and special sessions addressing algorithmic parts of EMO. Our major goals are to focus more on the new aspects such as theory of EMO, many-objective Optimization, dynamic EMO, Robustness in EMO, combinations of EMO and MCDM approaches and last but not least applications.

You are more than welcome to contribute!


What is Multi-Objective Optimization?

Multi-objective optimization refers to the solution of problems with two or more objectives to be satisfied simultaneously. Normally, such objectives are in conflict with each other and are expressed in different units. Because of their nature, multi-objective optimization problems normally have not one but a set of solutions, which are called Pareto optimal solutions. When such solutions are plotted in objective function space, the graph produced is called the Pareto front of the problem.


Why Evolutionary Multi-Objective Algorithms?

Despite the existence of numerous mathematical programming techniques for multi-objective optimization, evolutionary algorithms are particularly suitable for these problems because of several reasons:


Where can I start to learn about Multi-Objective Evolutionary Algorithms?

There are several tutorials available in electronic format. The following is a representative list:

Note that there is also a paper version of this tutorial. The full reference is the following:

Eckart Zitzler, Marco Laumanns and Stefan Bleuler. A Tutorial on Evolutionary Multiobjective Optimization, in Xavier Gandibleux, Marc Sevaux, Kenneth Sörensen and Vincent T'kindt (editors), Metaheuristics for Multiobjective Optimisation, pp. 3--37, Springer. Lecture Notes in Economics and Mathematical Systems Vol. 535, Berlin, 2004.

Note that there is also a paper version of this tutorial. The full reference is the following:

Carlos A. Coello Coello. A Short Tutorial on Evolutionary Multiobjective Optimization. In Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David Corne, editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 21-40. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.


Additional Resources

If you want to get more in-depth knowledge about multi-objective evolutionary algorithms, there are two monographs devoted to this topic currently available:



The EMOO repository contains a lot of valuable resources for those interested in multi-objective evolutionary algorithms, such as:


EMO Platforms and Programs:


Multi-Objective Optimization and Games

Here is an interesting application of EMO in Games: Multi-Objective Physical Traveling Salesman Problem



Some important upcoming events related to multi-objective evolutionary algorithms are the following:


Past Events:


For additional information

If you want to know more about the activities of this working group, or you want to join us, please contact:

Prof. Dr.-Ing. Sanaz Mostaghim
Faculty of Computer Science
University of Magdeburg

Dr. Marde Helbig

Senior Researcher, CSIR: Meraka Institute and
Temporary Senior Lecturer, University of Pretoria

South Africa

Dr. Rui Wang
College of Information Systems and Management
National University of Defense Technology