Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming Featuring Feynman Equations (CROSBI ID 694742)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Đurasević, Marko ; Jakobović, Domagoj ; Scoczynski Ribeiro Martins, Marcella ; Picek, Stjepan ; Wagner, Markus
engleski
Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming Featuring Feynman Equations
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 dimensionally- aware genetic programming search spaces on a subset of equations from Richard Feynman’s 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.
genetic programming ; dimensionally-aware GP ; fitness landscape ; local optima network
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Podaci o prilogu
111-124.
2020.
objavljeno
10.1007/978-3-030-58115-2_8
Podaci o matičnoj publikaciji
Lecture Notes in Computer Science
Podaci o skupu
Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020
predavanje
05.09.2020-09.09.2020
Liblice, Češka Republika