Improving evolutionary algorithms by enhancing an approximative fitness function through prediction intervals

Christina Plump; Bernhard J. Berger; Rolf Drechsler

In: IEEE Congress on Evolutionary Computation (CEC). IEEE Congress on Evolutionary Computation (IEEE CEC-2021), June 28 - July 1, Krakow/Virtual, Poland, 2021.


Evolutionary algorithms are a successful application of bio-inspired behaviour in the field of Artificial Intelligence. Transferring mechanisms such as selection, mutation, and recombination, evolutionary algorithms are capable of surmounting the disadvantages of traditional methods as—for example, hillclimbing—have. Adjusting an evolutionary algorithm to a specific problem requires both, a good understanding of the problem and deep knowledge of the effects of choosing one or another operator in the algorithm. This becomes an especially difficult task when the fitness function is not analytically given - that is, exists only as an approximation, that is highly dependent on the present training data. We propose using prediction intervals to modify the fitness function such, that worse fitness values are less penalized if they occur in a poorly fitted area. We evaluate this with an example from material sciences as well as four standard benchmark algorithms for evolutionary algorithms using a Support Vector Regression for training the approximative fitness function and find that our approach outperforms the naive approximative function. approximative function

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence