Primjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža

undergraduate thesis

• Title (english): Application of evolutionary algorithms for the design of neural network architectures
• Author: Dina Petrak
• Mentor: Marko Đurasević (mentor)
• Committee member: Marko Đurasević (predsjednik povjerenstva)
• Committee member: Nikolina Frid (član povjerenstva)
• Committee member: Zoran Medić (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

U današnje vrijeme, neuronske mreže sve su veći predmet interesa. Oponašanjem
funkcionalnosti i povezanosti neurona u mozgu, umjetne neuronske mreže mogu učiti
te raditi predikcije. Razvijene su mnoge tehnike učenja, a u ovom je radu opisano
učenje genetskim algoritmom, još poznato i kao neuroevolucija. Implementirane su
dvije neuronske mreže. U prvoj je genetski algoritam korišten samo za optimizaciju
težina, dok je u drugoj NEAT algoritam primijenjen za optimizaciju same arhitekture
neuronske mreže. Te su neuronske mreže korištene za rješavanje problema regresije.
Primjer rada neuronskih mreži prikazan je na četiri funkcije različite težine. Funkcije
easy, medium i hard generirane su kao primjer funkcija različitih težina u kojima sigurno
postoji ovisnost izlaza o ulazu dok za funkciju zraka ta povezanost nije jasna.
Na samome kraju prikazani su dobiveni rezultati.

Abstract (english)

Today, neural networks are an emerging topic of interest. By emulating the functions
and connections of neurons in the human brain, artificial neural networks are
able to learn and make predictions. While many different techniques can be used in
the process of learning, this work focuses on using genetic algorithms. That technique
is called neuroevolution. Two neural networks were implemented. The first one had a
fixed structure where only the weights were optimized by the genetic algorithm. For
the second one, genetic algorithm NEAT was used to improve the architecture. Neural
networks were then used to solve regression problems by making predictions of
four different functions. Easy, medium and hard functions are an example of functions
where the connection between outputs and inputs is certain and clear. That is not the
case with the air function. Results are shown in the final part of the work.

• Keywords: neuroevolucija; NEAT; optimizacija arhitekture; regresija
• Keywords (english): neuroevolution; NEAT; architecture optimization; regression.
• Language: croatian
• URN:NBN: urn:nbn:hr:168:466371
• 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 11:03:04