Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
On the Design of Optimization Strategies Based on Global Response Surface Approximation Models
Journal of Global Optimization
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Tuning space mapping: The state of the art
International Journal of RF and Microwave Computer-Aided Engineering
Optimal robust expensive optimization is tractable
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Comparison-based optimizers need comparison-based surrogates
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
On the effect of response transformations in sequential parameter optimization
Evolutionary Computation
Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es)
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Surrogate ranking in evolutionary computation using ordinal regression is introduced. The fitness of individual points is indirectly estimated by modeling their rank. The aim is to reduce the number of costly fitness evaluations needed for evolution. The ordinal regression, or preference learning, implements a kernel-defined feature space and an optimization technique by which the margin between rank boundaries is maximized. The technique is illustrated on some classical numerical optimization functions using an evolution strategy. The benefits of surrogate ranking, compared to surrogates that model the fitness function directly, are discussed.