Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Model selection based on minimum description length
Journal of Mathematical Psychology
Efficient Discretization Scheduling In Multiple Dimensions
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Actively probing and modeling users in interactive coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Comparison of tree and graph encodings as function of problem complexity
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary consequences of coevolving targets
Evolutionary Computation
iBOA: the incremental bayesian optimization algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
Coevolution of Fitness Predictors
IEEE Transactions on Evolutionary Computation
Symbolic regression of multiple-time-scale dynamical systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Co-evolutionary predictors for kinematic pose inference from RGBD images
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Many applications of evolutionary algorithms utilize fitness approximations, for example coarse-grained simulations in lieu of computationally intensive simulations. Here, we propose that it is better to learn approximations that accurately predict the ranks of individuals rather than explicitly estimating their real-valued fitness values. We present an algorithm that coevolves a rank-predictor which optimizes to accurately rank the evolving solution population. We compare this method with a similar algorithm that uses fitness-predictors to approximate real-valued fitnesses. We benchmark the two approaches using thousands of randomly-generated test problems in Symbolic Regression with varying difficulties. The rank prediction method showed a 5-fold reduction in computational effort for similar convergence rates. Rank prediction also produced less bloated solutions than fitness prediction.