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Elo-based similar-strength opponent sampling for multiobjec..:

, In: Proceedings of the Genetic and Evolutionary Computation Conference Companion,

Harris, Sean N. ; Tauritz, Daniel R. - p. 237-238 , 2021

Link: https://dl.acm.org/doi/10.1145/3449726.3459559

RT 
T1 <cTitle>Proceedings of the Genetic and Evolutionary Computation Conference Companion</cTitle>: T1 <Title>Elo-based similar-strength opponent sampling for multiobjective competitive coevolution</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3459559&Exemplar=1&LAN=DE
				
A1 Harris, Sean N.
A1 Tauritz, Daniel R.
PB ACM
YR 2021
K1 Elo rating system
K1 competitive co-evolution
K1 competitive coevolution
K1 evolutionary algorithms
K1 multiobjective evolutionary algorithms
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Bio-inspired approaches
K1 Genetic algorithms
K1 Applied computing
K1 Operations research
K1 Decision analysis
K1 Multi-criterion optimization and decision-making
K1 Theory of computation
K1 Design and analysis of algorithms
K1 Mathematical optimization
K1 Discrete optimization
K1 Optimization with randomized search heuristics
K1 Evolutionary algorithms
K1 Learning paradigms
K1 Reinforcement learning
K1 Adversarial learning
SP 237
OP 238
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3449726.3459559
DO https://dl.acm.org/doi/10.1145/3449726.3459559
SF ELIB - SuUB Bremen

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