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Koevoluce kartézských genetických algoritmů a neuronových sítí / Coevolution of Cartesian Genetic Algorithms and Neural Networks

The aim of the thesis is to verify synergy of genetic programming and neural networks. Solution is provided by set of experiments with implemented library built upon benchmark tasks. I've done experiments with directly and also indirectly encoded neural netwrok. I focused on finding robust solutions and the best calculation of configurations, overfitting detection and advanced stimulations of solution with fitness function. Generally better solutions were found using lower values of parameters n_c and n_r. These solutions tended less to be overfitted. I was able to evolve neurocontroller eliminating oscilations in pole balancing problem. In cancer detection problem, precision of provided solution was over 98%, which overcame compared techniques. I succeeded also in designing of maze model, where agent was able to perform multistep tasks.

Identiferoai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:236080
Date January 2014
CreatorsKolář, Adam
ContributorsKrál, Jiří, Zbořil, František
PublisherVysoké učení technické v Brně. Fakulta informačních technologií
Source SetsCzech ETDs
LanguageCzech
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/masterThesis
Rightsinfo:eu-repo/semantics/restrictedAccess

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