Thesis Details
Souběžné učení v kartézském genetickém programování
This thesis deals with the integration of co-learning into cartesian genetic programming. The task of symbolic regression was already solved by cartesian genetic programming, but this method is not perfect yet. It is relatively slow and for certain tasks it tends not to find the desired result. However with co-learning we can enhance some of these attributes. In this project we introduce a genotype plasticity, which is based on Baldwins effect. This approach allows us to change the phenotype of an individual while generation is running. Co-learning algorithms were tested on five different symbolic regression tasks. The best enhancement delivered in experiments by co-learning was that the speed of finding a result was 15 times faster compared to the algorithm without co-learning.
Co-learning, cartesian genetic programming, evolutionary algorithm, Baldwin effect, symbolic regression.
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Křivka Zbyněk, Ing., Ph.D. (DIFS FIT BUT), člen
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT), člen
Strnadel Josef, Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT18118,
author = "Jakub Korgo",
type = "Bachelor's thesis",
title = "Soub\v{e}\v{z}n\'{e} u\v{c}en\'{i} v kart\'{e}zsk\'{e}m genetick\'{e}m programov\'{a}n\'{i}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2016,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/18118/"
}