Adnan Acan received his B.Sc., M.Sc., and Ph.D. degrees from Middle East Technical University, Electrical and Electronic Engineering Department, Ankara, Turkey. He worked as a visiting scholar at The Ohio State University in Electrical Engineering Department in 1994 for one year. From 1995 to 2018, he was an assistant professor in Computer Engineering Department of Eastern Mediterranean University, North Cyprus.
Since May 2018, he is working as an associate professor in the same Department. His current interests include evolutionary computation, nature-inspired computation, metaheuristics, artificial life, neuro-fuzzy-evolutionary systems, multiagent systems, and optimization for computer vision and image processing.

Multiobjective optimization and the associated solution methods are hot research subjects in almost all fields of engineering and science. A multiobjective optimization problem include multiple objectives to be optimized (minimized or maximized) simultaneously. The objectives involved in problem definition are usually conflicting and a potential solution proposed for such a problem is a point of compromise such that one cannot improve one objective without sacrificing from another.

Solutions for multiobjective optimization problems (MOPs) can be categorized as exact solution methods and approximation solution methods. For general multi-dimensional MOPs, exact solution methods are usually computationally infeasible and quite rich sets approximation algorithms are proposed in literature. Among these approximation algorithms multiobjective evolutionary algorithms (MOEAs) are among the most successful and the most widely studied ones.