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

	Tobias Peter Using Deep Learning as a surrogate model in Multi-objective Evolutionary Algorithms DocID: 1uwn0 - View Document
	APPLICATION OF TWO-LEVEL EVOLUTIONARY ALGORITHMS TO SELF-STRUCTURING ANTENNAS C. M. Coleman*, E. J. Rothwell ECE Department Michigan State University East Lansing, MI 48824 DocID: 1uaTK - View Document
	A Gentle Introduction to the Time Complexity Analysis of Evolutionary Algorithms Pietro S. Oliveto Department of Computer Science The University of Sheffield, Sheffield, UK email: DocID: 1uaFl - View Document
	Evolutionary Algorithms for Quantum Computers Daniel Johannsen ∗ School of Mathematical Sciences DocID: 1rQ44 - View Document
	Creative Evolutionary Algorithms Francisco Fernandez de Vega University of Extremadura Abstract The Tutorial on Creative Evolutionary Algorithms (EAs) is aimed at CEC attendees with limited knowledge on the applications DocID: 1rOrd - View Document