Abstract
In this paper we present a novel fractal encoding scheme for genetic algorithms based on iterated function systems. The algorithm is capable of encoding self-similar search spaces of fractional dimensions - including spaces of measure zero. Such self-similar spaces can naturally arise in many optimisation problems. In the paper, we also discuss the relationships between the Cantor set and probabilistic spaces, and the potential application of Cantor Dust as a combination of probability trees to create hybrid models. We conduct an experiment and report the results in order to illustrate the idea of fractal encoding. Finally, we also discuss the potential application areas of this new proposed algorithm.
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Notes
- 1.
Genetic Algorithm is just one type of Evolutionary Algorithms.
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Acknowledgment
This work has been partially funded by the CERBERO project no. 732105 - a HORIZON 2020 EU project. CERBERO project aims at developing a design environment for Cyber Physical Systems based on two pillars: a cross-layer model based approach to describe, optimize, and analyze the system and all its different views concurrently; an advanced adaptivity support based on a multi-layer autonomous engine. AmbieSense works on the new type of marine robot with surface and underwater surveillance capabilities, which is one of CERBERO use cases. Within the context of the project we have been exploring novel optimisation and data fusion algorithms.
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Kaliciak, L., Myrhaug, H., Goker, A. (2019). Searching of Self-similar Spaces. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-02683-7_81
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DOI: https://doi.org/10.1007/978-3-030-02683-7_81
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