Excluding fitness helps improve robustness of evolutionary algorithms

Authors:
Matej Šprogar
Affiliations:
Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

Citing 4
Cited 1

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications

Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
An Evaluation of EvolutionaryGeneralisation in Genetic Programming

Artificial Intelligence Review
Improving the accuracy and robustness of genetic programming through expression simplification

GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation

Population variation in genetic programming

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The article describes a variant of evolutionary algorithms, which avoids the usual explicit fitness based cycle. The idea is to exclude any presumptions about the problem at hand in the coding of the artificial evolution. In nature the fitness is implicit and we created a similar environment for genetic programming to solve practical engineering problems. Our attempt to avoid direct (explicit) fitness calculation showed positive effects on the robustness of the evolved solutions.