Prof. Dr. Eckart Zitzler

"On Intelligent Optimization: Bio-inspired Computation, Multiple Criteria Decision Making and Artificial Intelligence"

Biologically-inspired computation, a branch of computational intelligence, is an umbrella term for different computational approaches that are based on principles or models of biological systems. This class of methods complements traditional techniques in the sense that the former can be applied to large-scale applications where little is known about the underlying problem and where the latter approaches encounter difficulties. Moreover, biologically motivated methods are ideally suited to model-oriented problem solving where learning about the problem as well as the modelling process as such are at the center of attention. These techniques have become indispensable instruments for tackling complex optimization problems and have been successfully used in various application domains for systems design and analysis.

A promising and rapidly expanding area of research is the employment of bio-inspired computation, in particular evolutionary algorithms, for multiobjective optimization scenarios which arise naturally in most real-world applications. Meanwhile, evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. This cross-fertilization has initiated a paradigm shift from automated optimization to interactive, user-guided problem solving principles combining search and decision making.

This talk highlights recent advances in the field of evolutionary multiobjective optimization, provides a personal view on the challenges ahead, and outlines connections and interfaces to other areas in the realm of artificial intelligence.