Primjena genski ekspresivnog programiranja na problemu simboličke regresije

undergraduate thesis

• Title (english): Application of gene expression programming for the symbolic regression problem
• Author: Tomislav Krog
• Mentor: Marko Đurasević (mentor)
• Committee member: Marko Đurasević (predsjednik povjerenstva)
• Committee member: Vjeko Kužina (član povjerenstva)
• Committee member: Mak Gračić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2022-07-07, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Tema ovog rada je „Primjena genski ekspresivnog programiranja na problemu simboličke regresije”. Implementacija genski ekspresivnog programiranja izvedena je pomoću alata geppy koji je zasnovan na DEAP frameworku za evolucijsko računarstvo. Preinakama gotovih algoritama geppy alata te isto tako dodavanjem inovacija u alat pokušava se riješiti središnji problem ovog rada – simbolička regresija. Drugim riječima, pokušava se pronaći simbolički izraz koji odgovara podacima iz nepoznate funkcije. Kao skup podataka u ovom radu koristi se baza od 100 zagonetki simboličke regresije iz udžbenika Richarda Feynmana „The Feynman Lectures on Physics”.

Abstract (english)

The topic of this paper is "Application of gene expression programming for the symbolic regression problem". The implementation of gene expression programming was performed using an evolutionary algorithm framework called geppy which is built on top of the evolutionary computing framework called DEAP. By modifying algorithms of the geppy tool and also by adding innovations to the tool, we tried to solve the central problem of this work - symbolic regression. In other words, an attempt is made to find a symbolic expression that corresponds to data from unknown functions. As a dataset in this paper, a database of 100 puzzles of symbolic regression from Richard Feynman's textbook "The Feynman Lectures on Physics" is used.

• Keywords: simbolička regresija; genski ekspresivno programiranje; strojno učenje; regresija; evolucijsko računarstvo
• Keywords (english): symbolic regression; gene expression programming; machine learning; regression; evolutionary computation
• Language: croatian
• URN:NBN: urn:nbn:hr:168:172918
• 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: 2023-01-15 10:37:45