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Hypervolume-Based Multi-Objective Reinforcement Learning
2013Indicator-based evolutionary algorithms are amongst the best performing methods for solving multi-objective optimization (MOO) problems. In reinforcement learning (RL), introducing a quality indicator in an algorithm’s decision logic was not attempted before.
Van Moffaert, Kristof +2 more
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Hypervolumes in microcanonical Monte Carlo
Computer Physics Communications, 1995A Monte Carlo method to perform microcanonical simulations by sampling the configurational and momenta spaces is presented. The technique was inspired by the method of hypervolumes for calculating the entropy in a microcanonical ensemble. Although this method is strictly proven in the thermodynamic limit, the hypervolume Monte Carlo (HVMC) algorithm ...
Fernando M.S.Silva Fernandes +1 more
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The American Naturalist, 2016
Hypervolumes are used widely to conceptualize niches and trait distributions for both species and communities. Some hypervolumes are expected to be convex, with boundaries defined by only upper and lower limits (e.g., fundamental niches), while others are expected to be maximal, with boundaries defined by the limits of available space (e.g., potential ...
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Hypervolumes are used widely to conceptualize niches and trait distributions for both species and communities. Some hypervolumes are expected to be convex, with boundaries defined by only upper and lower limits (e.g., fundamental niches), while others are expected to be maximal, with boundaries defined by the limits of available space (e.g., potential ...
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An Efficient Algorithm for Computing Hypervolume Contributions
Evolutionary Computation, 2010The hypervolume indicator serves as a sorting criterion in many recent multi-objective evolutionary algorithms (MOEAs). Typical algorithms remove the solution with the smallest loss with respect to the dominated hypervolume from the population. We present a new algorithm which determines for a population of size n with d objectives, a solution with ...
Karl, Bringmann, Tobias, Friedrich
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Updating exclusive hypervolume contributions cheaply
2009 IEEE Congress on Evolutionary Computation, 2009Several multi-objective evolutionary algorithms compare the hypervolumes of different sets of points during their operation, usually for selection or archiving purposes. The basic requirement is to choose a subset of a front such that the hypervolume of that subset is maximised.
Lucas Bradstreet +2 more
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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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Greedy Hypervolume Subset Selection in Low Dimensions
Evolutionary Computation, 2016Given a nondominated point set [Formula: see text] of size [Formula: see text] and a suitable reference point [Formula: see text], the Hypervolume Subset Selection Problem (HSSP) consists of finding a subset of size [Formula: see text] that maximizes the hypervolume indicator.
Andreia P, Guerreiro +2 more
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On the fast hypervolume calculation method
2015 IEEE Congress on Evolutionary Computation (CEC), 2015We propose a new algorithm FHV (Fast HyperVolume) for exact hypervolume (HV) calculation using divide and conquer algorithm. FHV divides the original set of non-dominated solutions into several fractions first, calculate the value of HV of each fraction separately, sum up the each value, and finally obtain the value of HV of the original set. Therefore
Takeshi Watanabe +2 more
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Normalization in R2-Based Hypervolume and Hypervolume Contribution Approximation
2023 IEEE Symposium Series on Computational Intelligence (SSCI), 2023Guotong Wu +3 more
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