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1.
DynFS: dynamic genotype cutting feature selection algorithm
Dušan Fister, Iztok Fister, Sašo Karakatič, 2023, izvirni znanstveni članek

Ključne besede: feature selection, nature-inspired algorithms, swarm intelligence, optimization
Objavljeno v DKUM: 05.04.2024; Ogledov: 137; Prenosov: 10
.pdf Celotno besedilo (1,14 MB)

2.
Construction of deep neutral networks using swarm intelligence to detect anomalies : master's thesis
Sašo Pavlič, 2021, magistrsko delo

Opis: The design of neural network architecture is becoming more difficult as the complexity of the problems we tackle using machine learning increases. Many variables influence the performance of a neural model, and those variables are often limited by the researcher's prior knowledge and experience. In our master's thesis, we will focus on becoming familiar with evolutionary neural network design, anomaly detection techniques, and a deeper knowledge of autoencoders and their potential for application in unsupervised learning. Our practical objective will be to build a neural architecture search based on swarm intelligence, and construct an autoencoder architecture for anomaly detection in the MNIST dataset.
Ključne besede: neural architecture search, machine learning, swarm intelligence
Objavljeno v DKUM: 18.10.2021; Ogledov: 1138; Prenosov: 98
.pdf Celotno besedilo (3,18 MB)
Gradivo ima več datotek! Več...

3.
A hybrid bat algorithm
Iztok Fister, Dušan Fister, Xin-She Yang, 2013, izvirni znanstveni članek

Ključne besede: swarm intelligence, bat algorithm, differential evolution, optimization
Objavljeno v DKUM: 21.12.2015; Ogledov: 2054; Prenosov: 27
URL Povezava na celotno besedilo

4.
A COMPREHENSIVE REVIEW OF BAT ALGORITHMS AND THEIR HYBRIDIZATION
Iztok Fister, 2013, magistrsko delo

Opis: Swarm intelligence is a modern and efficient mechanism for solving hard problems in computer science, engineering, mathematics, economics, medicine and optimization. Swarm intelligence is the collective behavior of decentralized and self-organized systems. This research area is a branch of artificial intelligence and could be viewed as some kind of family relationship with evolutionary computation because both communities share a lot of common characteristics. To date, a lot of swarm intelligence algorithms have been developed and applied to several real-world problems. The main focus of this thesis is devoted to the bat algorithm which is a member of the swarm intelligence community, as developed recently. In line with this, a comprehensive analysis of papers was performed tackling this algorithm. Some hybridizations of the original algorithm were proposed because the preliminary results of this algorithm regarding the optimization of benchmark functions with higher dimensions had not too promising. Extensive experiments showed that the hybridizing the original bat algorithm has beneficial effects on the results of the original bat algorithm. Finally, an experimental study was performed during which we researched for the dependence of an applied randomized method on the results of the original bat algorithm. The results of this study showed that selecting the randomized method had a crucial impact on the results of the original bat algorithm and that the bat algorithm using Levy flights is also suitable for solving the harder optimization problems.
Ključne besede: swarm intelligence, evolutionary computation, bat algorithm, hybridization, review
Objavljeno v DKUM: 06.09.2013; Ogledov: 3385; Prenosov: 326
.pdf Celotno besedilo (8,69 MB)

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