Naslov
Fitness Landscape Analysis of Dimensionally-Aware Genetic Programming Featuring Feynman Equations

Autori
Đurasević, Marko ; Jakobović, Domagoj ; Scoczynski Ribeiro Martins, Marcella ; Picek, Stjepan ; Wagner, Markus

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Lecture Notes in Computer Science / - , 2020, 111-124

Skup
Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020

Mjesto i datum
Liblice, Češka Republika, 05.09.2020. - 09.09.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
genetic programming ; dimensionally-aware GP ; fitness landscape ; local optima network

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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