Results 171 to 180 of about 1,756,488 (218)
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Annual Conference on Genetic and Evolutionary Computation, 2022
Multi-objective test case selection techniques are widely investigated with the goal of devising novel solutions to increase the cost-effectiveness of verification processes.
Aitor Arrieta
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Multi-objective test case selection techniques are widely investigated with the goal of devising novel solutions to increase the cost-effectiveness of verification processes.
Aitor Arrieta
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Two-dimensional subset selection for hypervolume and epsilon-indicator
Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014The goal of bi-objective optimization is to find a small set of good compromise solutions. A common problem for bi-objective evolutionary algorithms is the following subset selection problem (SSP): Given n solutions P ⊂ R2 in the objective space, select k solutions P* from P that optimize an indicator function.
Bringmann, K. ; https://orcid.org/0000-0003-1356-5177 +2 more
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Reliability of convergence metric and hypervolume indicator for many-objective optimization
2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC), 2016With the emergence and growth of Many-Objective Optimization algorithms, there has been an increased necessity to formulate new metrics that can perform quantitative assessment of the Pareto-Front returned as a solution from a Many-Objective Optimization algorithm.
Monalisa Pal, S. Bandyopadhyay
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Cone-Based Hypervolume Indicators: Construction, Properties, and Efficient Computation
2013In this paper we discuss cone-based hypervolume indicators (CHI) that generalize the classical hypervolume indicator (HI) in Pareto optimization. A family of polyhedral cones with scalable opening angle γ is studied. These γ-cones can be efficiently constructed and have a number of favorable properties.
Emmerich, Michael +3 more
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Analyzing Hypervolume Indicator Based Algorithms
2008Indicator-based methods to tackle multiobjective problems have become popular recently, mainly because they allow to incorporate user preferences into the search explicitly. Multiobjective Evolutionary Algorithms (MOEAs) using the hypervolume indicator in particular showed better performance than classical MOEAs in experimental comparisons.
Brockhoff, D. +2 more
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Time Complexity and Zeros of the Hypervolume Indicator Gradient Field
A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation, 2012In multi-objective optimization the hypervolume indicator is a measure for the size of the space within a reference set that is dominated by a set of μ points. It is a common performance indicator for judging the quality of Pareto front approximations.
M. Emmerich, A. Deutz
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International Conference on Evolutionary Multi-Criterion Optimization, 2007
The design of quality measures for approximations of the Pareto-optimal set is of high importance not only for the performance assessment, but also for the construction of multiobjective optimizers. Various measures have been proposed in the literature with the intention to capture different preferences of the decision maker.
E. Zitzler, D. Brockhoff, L. Thiele
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The design of quality measures for approximations of the Pareto-optimal set is of high importance not only for the performance assessment, but also for the construction of multiobjective optimizers. Various measures have been proposed in the literature with the intention to capture different preferences of the decision maker.
E. Zitzler, D. Brockhoff, L. Thiele
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ACM Transactions on Evolutionary Learning and Optimization
To address problems with multiple conflicting objective functions while incorporating preference information from a decision maker (a domain expert), interactive evolutionary multiobjective optimization methods have been proposed and widely adopted.
Maomao Liang +3 more
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To address problems with multiple conflicting objective functions while incorporating preference information from a decision maker (a domain expert), interactive evolutionary multiobjective optimization methods have been proposed and widely adopted.
Maomao Liang +3 more
semanticscholar +1 more source
Generic Postprocessing via Subset Selection for Hypervolume and Epsilon-Indicator
2014Most biobjective evolutionary algorithms maintain a population of fixed size μ and return the final population at termination. During the optimization process many solutions are considered, but most are discarded. We present two generic postprocessing algorithms which utilize the archive of all non-dominated solutions evaluated during the search.
Bringmann, K. ; https://orcid.org/0000-0003-1356-5177 +2 more
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