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.

Letzte Änderung: 16.05.2016 - Contact Person: Webmaster