Abstract
This paper proposes some algorithmic extensions to the general concept of offspring selection which itself is an algorithmic extension of genetic algorithms and genetic programming. Offspring selection is characterized by the fact that many offspring solution candidates will not participate in the ongoing evolutionary process if they do not achieve the success criterion. The algorithmic enhancements proposed in this contribution aim to early estimate if a solution candidate will not be accepted based on partial solution evaluation. The qualitative characteristics of offspring selection are not affected by this means. The discussed variant of offspring selection is analyzed for several symbolic regression problems with offspring selection genetic programming. The achievable gains in terms of efficiency are remarkable especially for large data-sets.
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Acknowledgments
The work described in this paper was done within the COMET Project #843532 Heuristic Optimization in Production and Logistics (HOPL) funded by the Austrian Research Promotion Agency (FFG) and the Government of Upper Austria and the COMET Project #843551 Advanced Engineering Design Automation (AEDA) funded by the Austrian Research Promotion Agency (FFG) and the Government of Vorarlberg.
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Affenzeller, M., Burlacu, B., Winkler, S., Kommenda, M., Kronberger, G., Wagner, S. (2018). Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_51
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