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1.
Nature-inspired algorithms for hyperparameter optimization : magistrsko delo
Filip Glojnarić, 2019, magistrsko delo

Opis: This master thesis is focusing on the utilization of nature-inspired algorithms for hyperparameter optimization, how they work and how to use them. We present some existing methods for hyperparameter optimization as well as propose a novel method that is based on six different nature-inspired algorithms: Firefly algorithm, Grey Wolf Optimizer, Particle Swarm Optimization, Genetic algorithm, Differential Evolution, and Hybrid Bat algorithm. We also show the optimization results (set of hyperparameters) for each algorithm and we present the plots of the accuracy for each combination and handpicked one. In discussion of the results, we provide the answers on our research questions as well as propose ideas for future work.
Ključne besede: artificial intelligence, artificial neural networks, machine learning, nature-inspired algorithms, evolutionary algorithms
Objavljeno v DKUM: 09.12.2019; Ogledov: 1011; Prenosov: 87
.pdf Celotno besedilo (969,13 KB)

2.
Šahovski sistem rangiranja za primerjavo evolucijskih algoritmov : doktorska disertacija
Niki Veček, 2016, doktorska disertacija

Opis: Eksperiment na področju evolucijskega računanja lahko povzamemo s štirimi pomembnimi koraki: načrtovanje eksperimenta, zagon eksperimenta, analiza rezultatov ter interpretacija rezultatov in diskusija. Vsak korak zahteva posebno pozornost in vsebuje določene pasti, na katere moramo kot raziskovalci biti pozorni. Disertacija podrobno opiše vse štiri korake, s posebnim poudarkom na statistični analizi rezultatov, in predstavi novo metodo za primerjavo evolucijskih algoritmov - Chess Rating System for Evolutionary Algorithms (CRS4EAs). Predlagana metoda temelji na šahovskem rangiranju, kjer je vsak evolucijski algoritem predstavljen kot šahovski igralec, vsaka primerjava rešitev dveh algoritmov predstavlja igro med dvema igralcema (in se lahko konča z zmago enega in porazom drugega ali remijem), vsaka parna primerjava med več algoritmi pa predstavlja turnir. Osnova za predlagano metodo je šahovski sistem rangiranja Glicko-2, za katerega tekom disertacije tudi pokažemo, da je najprimernejši. Predlagano metodo skozi velik nabor eksperimentov primerjamo s statističnimi testi z ničelno hipotezo in pokažemo, da lahko s predlagano metodo učinkovito primerjamo uspešnosti evolucijskih algoritmov. Predlagana metoda najde podobne signifikantne razlike kot bi jih našli z uporabo standardnih statističnih metod, hkrati pa omogoča absolutno vrednotenje moči in uspešnosti algoritmov, ki so vključeni v sistem. Predlagano metodo na učinkovit način uporabimo za uglaševanje parametrov evolucijskega algoritma in jo skozi nabor več eksperimentov primerjamo z drugimi metodami uglaševanja (F-Race in Revac).
Ključne besede: evolutionary algorithms, computational experiment, null hypothesis, glicko, chess rating
Objavljeno v DKUM: 14.09.2016; Ogledov: 1321; Prenosov: 170
.pdf Celotno besedilo (15,24 MB)

3.
Predicting defibrillation success by "genetic" programming in patients with out-of-hospital cardiac arrest
Matej Podbregar, Miha Kovačič, Aleksandra Podbregar-Marš, Miran Brezočnik, 2003, izvirni znanstveni članek

Opis: In some patients with ventricular fibrillation (VF) there may be a better chance of successful defibrillation after a period of chest compression and ventilation before the defibrillation attempt. It is therefore important to know whether a defibrillation attempt will be successful. The predictive powerof a model developed by "genetic" programming (GP) to predict defibrillation success was studied. Methods and Results: 203 defibrillations were administered in 47 patients with out-of-hospital cardiac arrest due to a cardiac cause. Maximal amplitude, a total energy of power spectral density, and the Hurst exponent of the VF electrocardiogram (ECG) signal were included in the model developed by GP. Positive and negative likelihood ratios of the model for testing data were 35.5 and 0.00, respectively. Using a model developed by GP on the complete database, 120 of the 124 unsuccessful defibrillations would have been avoided, whereas all of the 79 successful defibrillations would have been administered. Conclusion: The VF ECG contains information predictive of defibrillation success. The model developed by GP, including data from the time-domain, frequency-domain and nonlinear dynamics, could reduce the incidence of unsuccessful defibrillations.
Ključne besede: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction
Objavljeno v DKUM: 01.06.2012; Ogledov: 1442; Prenosov: 84
URL Povezava na celotno besedilo

4.
A model of data flow in lower CIM levels
Igor Drstvenšek, Ivo Pahole, Jože Balič, 2004, izvirni znanstveni članek

Opis: After years of work in fields of computer-integrated manufacturing (CIM), flexible manufacturing systems (FMS), and evolutionary optimisation techniques, several models of production automation were developed in our laboratories. The last model pools the discoveries that proved their effectiveness in the past models. It is based on the idea of five levels CIM hierarchy where the technological database (TDB) represents a backbone of the system. Further on the idea of work operation determination by an analyse of the production system is taken out of a model for FMS control system, and finally the approach to the optimisation of production is supported by the results of evolutionary based techniques such as genetic algorithms and genetic programming.
Ključne besede: computer integrated manufacturing, flexible manufacturing systems, evolutionary optimisation techniques, production automation, CIM hierarchy, technological databases, production optimisation, genetic algorithms, genetic programming
Objavljeno v DKUM: 01.06.2012; Ogledov: 1639; Prenosov: 88
URL Povezava na celotno besedilo

5.
Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling
Petra Žohar, Miha Kovačič, Miran Brezočnik, Matej Podbregar, 2005, izvirni znanstveni članek

Opis: Atrial fibrillation (AF) is the most common rhythm disorder. Because of the high recurrence rate of AF after cardioversion and because of potential side effects of electrical cardioversion, it is clinically important to predict persistence of sinus rhythm after electrical cardioversion before it is attempted. The aim of our study was the development of a mathematical model by"genetic" programming (GP), a non-deterministic modelling technique, which would predict maintenance of sinus rhythm after electrical cardioversion of persistent AF. PATIENTS AND METHODS: Ninety-seven patients with persistent AF lasting more than 48 h, undergoing the first attempt at transthoracic cardioversion were included in this prospective study. Persistence of AF before the cardioversion attempt, amiodarone treatment, left atrial dimension,mean, standard deviation and approximate entropy of ECG R-R intervals were collected. The data of 53 patients were randomly selected from the database and used for GP modelling; the other 44 data sets were used for model testing. RESULTS: In 23 patients sinus rhythm persisted at 3 months. In the other 21 patients sinus rhythm was not achieved or its duration was less than 3 months. The model developed by GP failed to predict maintenance ofsinus rhythm at 3 months in one patient and in six patients falsely predicted maintenance of sinus rhythm. Positive and negative likelihood ratiosof the model for testing data were 4.32 and 0.05, respectively. Using this model 15 of 21 (71.4%) cardioversions not resulting in sinus rhythm at 3 months would have been avoided, whereas 22 of 23 (95.6%) cardioversions resulting in sinus rhythm at 3 months would have been administered. CONCLUSION: This model developed by GP, including clinical data, ECG data from the time-domain and nonlinear dynamics can predict maintenance of sinus rhythm. Further research is needed to explore its utility in the present or anexpanded form.
Ključne besede: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction, atrial fibrillation, electrical cardioversion, prediction
Objavljeno v DKUM: 01.06.2012; Ogledov: 1761; Prenosov: 72
URL Povezava na celotno besedilo

6.
7.
Hybridization of evolutionary algorithms
Iztok Fister, Marjan Mernik, Janez Brest, 2011, samostojni znanstveni sestavek ali poglavje v monografski publikaciji

Ključne besede: hybridization, evolutionary algorithms, heuristic algorithms
Objavljeno v DKUM: 01.06.2012; Ogledov: 1715; Prenosov: 76
URL Povezava na celotno besedilo

8.
Design of row-based flexible manufacturing system with evolutionary computation
Mirko Ficko, Jože Balič, 2008, objavljeni znanstveni prispevek na konferenci

Opis: This paper discusses design of flexible manufacturing systems (FMSs) in one or multiple rows. Evolutionary computation, particularly genetic algorithms (GAs) proved to be successful in search of optimal solution for this type of problems. The model of solution, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In this connection, the most favourable number of rows and the sequence of devices in the individual row are established by means of genetic algorithms (GAs). In the end the test results of the application made and the analysis are discussed.
Ključne besede: flexible manufacturing system design, genetic algorithms, evolutionary computation, intelligent manufacturing systems, artificial intelligence
Objavljeno v DKUM: 31.05.2012; Ogledov: 1656; Prenosov: 88
URL Povezava na celotno besedilo

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