Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming
Featuring Feynman Equations
- URL: http://arxiv.org/abs/2004.12762v1
- Date: Mon, 27 Apr 2020 13:05:28 GMT
- Title: Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming
Featuring Feynman Equations
- Authors: Marko Durasevic, Domagoj Jakobovic, Marcella Scoczynski Ribeiro
Martins, Stjepan Picek, and Markus Wagner
- Abstract summary: We conduct a fitness landscape analysis of dimensionallyaware genetic programming search spaces on a subset of equations from Richard Feynmans well-known lectures.
Our experiments show that the added information about the variable dimensionality can efficiently guide the search algorithm.
- Score: 8.477138002183713
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Genetic programming is an often-used technique for symbolic regression:
finding symbolic expressions that match data from an unknown function. To make
the symbolic regression more efficient, one can also use dimensionally-aware
genetic programming that constrains the physical units of the equation.
Nevertheless, there is no formal analysis of how much dimensionality awareness
helps in the regression process. In this paper, we conduct a fitness landscape
analysis of dimensionallyaware genetic programming search spaces on a subset of
equations from Richard Feynmans well-known lectures. We define an
initialisation procedure and an accompanying set of neighbourhood operators for
conducting the local search within the physical unit constraints. Our
experiments show that the added information about the variable dimensionality
can efficiently guide the search algorithm. Still, further analysis of the
differences between the dimensionally-aware and standard genetic programming
landscapes is needed to help in the design of efficient evolutionary operators
to be used in a dimensionally-aware regression.
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