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Title:METODE NEVROEVOLUCIJE
Authors:Mađarić, Sašo (Author)
Strnad, Damjan (Mentor) More about this mentor... New window
Guid, Nikola (Co-mentor)
Files:.pdf MAG_Madaric_Saso_2013.pdf (5,83 MB)
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Pri klasičnem razvoju nevronskih mrež za uporabo na različnih področjih umetne inteligence se pogosto srečujemo s problemom določanja optimalne topologije nevronske mreže, ki ima velik vpliv na njeno kvaliteto. V tem magistrskem delu smo se ukvarjali s problemom avtomatiziranega razvoja topologije nevronske mreže in njenega učenja z nevroevolucijskimi metodami. Področje, ki se ukvarja z razvojem nevronskih mrež s pomočjo evolucijskega algoritma, je nevroevolucija. Preučili in implementirali smo nevroevolucijske metode NEAT, HyperNEAT in ES-HyperNEAT. Uspešnost metod smo preizkusili na eksperimentu z agenti. Agent se nahaja v okolju in poskuša pobrati čim več kosov hrane ter se izogniti sovražnikom oziroma zidovom. Nevroevolucijske metode smo primerjali z metodo Q-učenje, ki za učenje nevronske mreže uporablja klasično metodo vzvratnega prenosa napake. Primerjali smo doseženo oceno in časovno zahtevnost. Rezultati so pokazali, da je najuspešnejša metoda HyperNEAT, sledita pa ji ES-HyperNEAT in NEAT. Metoda Q-učenje se je izkazala za najmanj uspešno, saj je glede na nevroevolucijske metode v podrejenem položaju tako po doseženi kriterijski oceni kot tudi po časovni zahtevnosti.
Keywords:umetna inteligenca, nevroevolucija, evolucijski algoritem, nevronske mreže, razvoj nevronskih mrež, NEAT, HyperNEAT, ES-HyperNEAT
Year of publishing:2013
Publisher:[S. Mađarić]
Source:Maribor
UDC:004.032.26:004.832.3(043.3)
COBISS_ID:17335318 Link is opened in a new window
NUK URN:URN:SI:UM:DK:1OF1WLB5
Views:1279
Downloads:129
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:NEUROEVOLUTION METHODS
Abstract:In classical development of neural networks for use in different areas of artificial intelligence we are often faced with the problem of optimal neural network topology selection, which has strong impact on neural network quality. In this master work we were studying with problem of automated development and training neural network topologies using neuroevolution methods. Development of NN topology using evolutionary algorithm is called nevroevolution. We have studied and implemented neuroevolution methods NEAT, HyperNEAT, and ES-HyperNEAT. The methods were compared using an experiment, where an agent tries to collect food and avoid enemies and walls. Neuroevolutionary methods were compared with the Q-learning, which uses backpropagation algorithm for neural network learning. The comparison was based on achieved fitness and time complexity. Results show, that the best method is HyperNEAT, followed by ES-HyperNEAT and NEAT. Method Q-learning method was least successful in both the achieved fitness and time complexity.
Keywords:artificial intelligence, nevroevolution, evolutionary algorithm, neural network, neural networks development, NEAT, HyperNEAT, ES-HyperNEAT


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