Efficient Optimization of Many Objectives by Approximation-Guided Evolution Markus Wagnerc,∗, Karl Bringmanna , Tobias Friedrichb , Frank Neumannc a Max-Planck-Institut f¨

First Page		Document Content
Date: 2016-01-03 06:48:47 Mathematical optimization Computational complexity theory Theory of computation Applied mathematics Academia Evolutionary algorithms Operations research Cybernetics Multi-objective optimization Reduction Algorithm CMA-ES		Efficient Optimization of Many Objectives by Approximation-Guided Evolution Markus Wagnerc,∗, Karl Bringmanna , Tobias Friedrichb , Frank Neumannc a Max-Planck-Institut f¨ Add to Reading List Source URL: people.mpi-inf.mpg.de Download Document from Source Website File Size: 673,45 KB Share Document on Facebook

	Journal of Global Optimization manuscript No. (will be inserted by the editor) Stabilizer-based symmetry breaking constraints for mathematical programs Leo Liberti · James Ostrowski DocID: 1v0h9 - View Document
	OPTIMA 88 Mathematical Optimization Society Newsletter Philippe L. Toint MOS Chair’s Column DocID: 1uTp0 - View Document
	The Annals of Probability 2004, Vol. 32, No. 1B, 1030–1067 © Institute of Mathematical Statistics, 2004 A STOCHASTIC REPRESENTATION THEOREM WITH APPLICATIONS TO OPTIMIZATION AND OBSTACLE PROBLEMS DocID: 1sOP9 - View Document
	Optimization of Electrical Production The production of electricity in France is optimized everyday with the help of a mathematical software developed at Inria, in collaboration with EDF R&D. Substantial performance is a DocID: 1rxID - View Document
	Timed-Elastic-Bands for Time-Optimal Point-To-Point Nonlinear Model Predictive Control DocID: 1ru4i - View Document