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

- Sanaz Mostaghim
(chair)
- Marde Helbig (vice chair)
- Rui Wang (vice
chair)
- Gideon Avigad
- Xinye Cai
- Wei-neng Cheng
- Carlos Coello Coello
- Kalyanmoy Deb
- Juan J. Durillo
- Maoguo Gong
- Hisao Ishibuchi
- Yaochu Jin
- Li Ke
- Hongtao Lei
- Hui Li
- Hong Li
- Jian-Ping Li
- Hailin Liu
- Yajie Liu
- Kaisa Miettinen
- Massimiliano Vasile
- Yong Wang
- Yuping Wang
- Jian Xiong
- Qingfu Zhang
- Jun Zhang
- Tao Zhang
- Aimin Zhou

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!**

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.

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

- Evolutionary
algorithms are less susceptible to the shape or continuity of the Pareto
front, whereas many mathematical programming techniques rely on some a
priori knowledge about such shape.

- Evolutionary
algorithms are population-based. Thus, it is expected that they can
produce several elements of the Pareto optimal set within a single
execution. In contrast, mathematical programming techniques normally
produce a single solution per run.

- Evolutionary
algorithms start with a set of random solutions, whereas mathematical
programming techniques normally require a starting point and the result
that they produce tends to rely on such point.

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

- The tutorial on
Evolutionary Multiobjective Optimization at CEC
2013, by Kalyanmoy Deb (tutorial slides)

- The tutorial on
Evolutionary Multi-objective Optimization in GECCO 2013, by Dimo Brockhoff (tutorial
slides)

- A Tutorial on
Evolutionary Multi-objective Optimization (2003), by Eckart
Zitzler (tutorial
slides).

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.

- A Short Tutorial
on Evolutionary Multiobjective Optimization
(2001), by Carlos A. Coello Coello
(tutorial
slides).

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.

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

- Branke, J., Deb, K., Miettinen, K., Slowinski, R.
(eds.), Multiobjective Optimization: Interactive
and Evolutionary Approaches, Springer-Verlag,
Berlin, Heidelberg, 2008
- Carlos A. Coello Coello, David A. Van Veldhuizen and Gary B. Lamont, Evolutionary
Algorithms for Solving Multi-Objective Problems, Kluwer Academic
Publishers, New York, March 2002, ISBN 0-3064-6762-3.
- Kalyanmoy Deb. Multi-Objective
Optimization using Evolutionary Algorithms, John Wiley & Sons, Chichester, UK, 2001, ISBN 0-471-87339-X.

**Webpages:**

**EMOO repository:**There is a webpage created by Carlos Coello Coello which is maintained since late 1998:http://delta.cs.cinvestav.mx/~ccoello/EMOO

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

- Over 2500
bibliographic references, many of which are electronically available. This
includes about 150 PhD theses, about 650 journal papers and about 1400
conference papers.
- Contact
information of about 65 researchers working in this area.
- Public-domain
versions of several multi-objective evolutionary algorithms, including
MOGA, MOPSO, NSGA, NSGA-II, microGA, among
others.
- Test functions
and sample Pareto fronts for well-known problems.

- http://www.soft-computing.de/ maintained by Yaochu
Jin, and shares some information on EC methods.

- http://www.macs.hw.ac.uk/~ml355/conference.htm
maintained by Michael A. Lones, and provides a
(almost complete) list of EC related conferences.

- PISA http://www.tik.ee.ethz.ch/pisa/?page=pisa.php
- JMetal
http://jmetal.sourceforge.net/index.html
- Liger
http://codem.group.shef.ac.uk/index.php/research/software

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:

- IEEE SSCI
2015: Our task force organizes one special session about
Evolutionary Multi-Objective Optimization (Organizers: Sanaz Mostaghim and
Marde Helbig)
- IEEE WCCI2016:
The annual IEEE Congress on Evolutionary Computation -> Our task force
organizes one special session about Evolutionary Multi-Objective
Optimization (Organizers: Sanaz Mostaghim and Kalyanmoy
Deb)

Past Events:

- SSCI 2014: 2014 IEEE Symposium on
Computational Intelligence in Multicriteria
Decision-Making (IEEE MCDM'2014) -> Our task force organizes two
special sessions:
- Special Sessions Optimization Methods in
Bioinformatics and Bioengineering (OMBB) (Organizers:Richard Allmendinger,
Anna Lavygina, and Sanaz Mostaghim )
- Evolutionary Multi-Objective Optimization (Organizers:Sanaz Mostaghim, Marde Helbig, Rui Wang )

- IEEE CEC 2015:
The annual IEEE Congress on Evolutionary Computation -> Our task force
organizes one competition
about Dynamic Evolutionary Multi-Objective Optimization (Organizers: Marde Helbig, Andries Engelbrecht and
Sanaz Mostaghim)
- IEEE CEC 2015:
The annual IEEE Congress on Evolutionary Computation -> Our task force
organizes one special session about Dynamic Evolutionary
Multi-Objective Optimization (Organizers: Marde Helbig, Andries Engelbrecht and Rui Wang)

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

Germany

Sanaz.mostaghim@ovgu.de

Dr. Marde Helbig

Senior Researcher, CSIR: Meraka Institute and

Temporary Senior Lecturer, University of Pretoria

South Africa

mgreeff@googlemail.com

Dr. Rui Wang

Lecturer

College of Information Systems and Management

National University of Defense Technology

China

ruiwangnudt@gmail.com