André DeutzLeiden University, Netherlands

Leiden Institute of Advanced Computer Science, Leiden University, Netherlands

Featured talk

On Steering Dominated points in hypervolume gradient ascent for Bicriteria Continuous Optimization

Abstract 

Bio

Research Interests

The current research focus of Dr.André Deutz is Multiobjective Optimization, also in the light of Natural Computing, more specifically Indicators and Multiobjective Optimization (and its generalizations such as Diversity oriented Optimization); secondly Geometric Algebra and its applications to Multiobjective Optimization and Quantum Computation.

Education 

André studied pure mathematics at the University of Amsterdam, specialization Algebraic Geometry (MSc cum laude). He wrote a doctoral thesis on Algebraic Topology at Wayne State University and UCLA. Subsequently he was invited by the Mathematical Association of America to participate in a 14-months study of Computer Science for ph.d. mathematicians. Furthermore he studied Computer Science at Cornell University (MSc).

Teaching 

Dr. Deutz has taught most of the courses of the standard curriculum of undergraduate Computer Science. He also taught graduate courses. Currently he is teaching Computer Systems for undergraduates and Quantum Computing for master level students.

Grants 

Fulbright grant (1977-1981), MAA invitation to study Computer Science (1984-1985), Dana Grant at Cornell University (1987-1988), Several Development Grants,

Publications 

[1] M. Emmerich, A. Deutz, O. Schutze, Th. B ̈ack, E. Tantar, A.-A. Tantar, P. Del Moral, P. Legrand, P. Bouvry, and C.A. Coello Coello, editors. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV International Conference Held at Leiden University, July 10-13 2013, XIV, 324 p. 140 illus., volume Vol. 227 of Advances in Intelligent Systems and Computing, Berlin Heidelberg, May 2013. Springer.


[2] Michael Emmerich and André Deutz, editors. EVOLVE 2013 - A Bridge Between Probability. Set Oriented Numerics, and Evolutionary Computation - Short Paper and Extended Abstract Proceedings. LIACS, Leiden University, July 2013.

[3] Michael Emmerich and André Deutz. Time complexity and zeros of the hypervolume indicator gradient field. In Oliver Schutze, Carlos A. Coello Coello, Alexandru-Adrian Tantar, Emilia Tantar, Pascal Bouvry, Pierre Del Moral, and Pierrick Legrand, editors, EVOLVE - A Bridge
between Probability, Set Oriented Numerics, and Evolutionary Computation III, volume 500 of Studies in Computational Intelligence, pages 169–193. Springer International Publishing, 2014.

[4] Michael Emmerich, André Deutz, and Johannes Kruisselbrink. On quality indicators for black-box level set approximation. In E. Tantar et al., editor, EVOLVE- A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation, volume 447 of SCI, pages 157–185. Springer, 2013.


[5] Michael Emmerich, André Deutz, Johannes Kruisselbrink, and Edgar Reehuis. Evolutionary level set approximation. In EVOLVE 2012, A Bridge between Probability, Set-oriented Numerics, and Evolutionary Computation. Cinvestav, 2012.


[6] Michael Emmerich, André Deutz, and Iryna Yevseyeva. On reference point free weighted hypervolume indicators based on desirability functions and their probabilistic interpretation. In CENTERIS. Elsevier, 2014.


[7] Michael Emmerich, André H. Deutz, Johannes W. Kruisselbrink, and Pradyumn Kumar Shukla. Cone-based hypervolume indicators: Construction, properties, and efficient computation. In Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco, and Jane Shaw, editors, EMO, volume 7811 of Lecture Notes in Computer Science, pages 111–127. Springer, 2013.


[8] Michael T. Emmerich, André H. Deutz, and Jan Willem Klinkenberg. Hypervolume-based expected improvement: Monotonicity properties and exact computation. In IEEE Congress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 5-8 June, 2011, pages 2147–
2154. IEEE, 2011.


[9] Pradyumn Kumar Shukla, Michael Emmerich, and André H. Deutz. A theoretical analysis of curvature based preference models. In Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco, and Jane Shaw, editors, EMO, volume 7811 of Lecture Notes in Computer Science, pages 367–382. Springer, 2013.

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