on September 8, 2015 at 3:00 pm
Multi-objective evolutionary algorithms (MOEAs) are widely used in practice for solving multi-objective design and optimization problems. Historically, most applications of MOEAs have dealt with two and three objective problems, leading to the development of several evolutionary approaches that work successfully in these low dimensional objective spaces. Recently, there is a growing interest in industry to solve problems that require the simultaneous optimization of four or more objectives, known as many-objective optimization problems. However, conventional MOEAs scale up poorly with the number of objectives of the problem. The development of robust, scalable, many-objective optimizers is an ongoing effort and a promising line of research. Critical to the development of such algorithms is an understanding of fundamental features of many-objective landscapes and the interaction between selection, variation, and population size to appropriately support the evolutionary search in high-dimensional spaces. This talk will give an introduction to evolutionary many-objective optimization, discussing some characteristics of many-objective landscapes and relating them to working principles, performance and behavior of the optimizers. It will also present a general overview of the approaches to many-objective optimization, together with their state-of-the-art algorithms and techniques. Further, it will illustrate the use of many-objective optimization for real-world applications.
Time and location: Tuesday, September 8 at 15:00, Amphi Turing (M3).
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