The attainment function provides a description of the location of the distribution of a random non-dominated point set. This function can be estimated from experimental data via its empirical counterpart, the empirical attainment function (EAF). However, computation of the EAF in more than two dimensions is a non-trivial task. In this article, the problem of computing the empirical attainment function is formalised, and upper and lower bounds on the corresponding number of output points are presented. In addition, efficient algorithms for the two and three-dimensional cases are proposed, and their time complexities are related to lower bounds derived for each case.
Conference
Sixth International Conference on Evolutionary Multi-Criterion Optimization (EMO 2011), LNCS 6576, 106-190, Springer, April 2011
Cited by
Year 2015 : 5 citations
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Multi-Objective Optimization in Multiagent Systems
PhD thesis, Oregon State University
Year 2014 : 5 citations
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I Tsoukalas, C Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling & Software, 2014
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FMO no Guzmán, IR Ruiz, Development of Advanced Computational Systems for Multiple Sequence Alignments by using Heterogeneous Biological Information, Doctoral Thesis - Francisco M Ortuno Guzman, 2014
Year 2013 : 1 citations
T. Tušar and B. Filipic, "An approach to visualizing the 3D empirical attainment function," in GECCO '13 Companion Proceedings, Fifteenth Annual Conference on Genetic and Evolutionary Computation (C. Blum, ed.), pp. 1367-1372, 2013.
Year 2012 : 2 citations
Michael P. Cipold, Pradyumn Kumar Shukla, Claus C. Bachmann, Kaibin Bao, Hartmut Schmeck , An Evolutionary Optimization Approach for Bulk Material Blending Systems, Parallel Problem Solving from Nature - PPSN XII
D Brockhoff, K Deb, GECCO 2012 tutorial on evolutionary multiobjective optimization, Proceedings of the fourteenth international conference on Genetic and evolutionary computation
