Primjena genetičkog programiranja za simboličku regresiju

undergraduate thesis

• Title (english): Application of genetic programming for symbolic regression
• Author: Leon Novački
• Mentor: Marko Đurasević (mentor)
• Committee member: Marko Đurasević (predsjednik povjerenstva)
• Committee member: Lucija Planinić (član povjerenstva)
• Committee member: Nikolina Frid (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2021-09-09, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Ovaj rad opisuje problem simboličke regresije. Objašnjava genetsko programiranje
i opisuje njegove korake. Sadrži implementaciju genetskog programiranja u programskom
jeziku Python te rezultate pri korištenju njega na sintetskim podatcima.

Abstract (english)

This thesis describes the problem of symbolic regression. It contains a description
of genetic programming and it’s main operations. Contains an implementation in the
programming language Python and it’s results on synthetic data.

• Keywords: genetsko programiranje; genetski algoritam; simbolička regresija
• Keywords (english): genetic programming; genetic algorithm; symbolic regression
• Language: croatian
• URN:NBN: urn:nbn:hr:168:352519
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-14 21:18:32