1. Attraction basins in metaheuristics: a systematic mapping studyMihael Baketarić, Marjan Mernik, Tomaž Kosar, 2021, review article Abstract: Context: In this study, we report on a Systematic Mapping Study (SMS) for attraction
basins in the domain of metaheuristics. Objective: To identify research trends, potential issues,
and proposed solutions on attraction basins in the field of metaheuristics. Research goals were
inspired by the previous paper, published in 2021, where attraction basins were used to measure
exploration and exploitation. Method: We conducted the SMS in the following steps: Defining research
questions, conducting the search in the ISI Web of Science and Scopus databases, full-text screening,
iterative forward and backward snowballing (with ongoing full-text screening), classifying, and data
extraction. Results: Attraction basins within discrete domains are understood far better than those
within continuous domains. Attraction basins on dynamic problems have hardly been investigated.
Multi-objective problems are investigated poorly in both domains, although slightly more often
within a continuous domain. There is a lack of parallel and scalable algorithms to compute attraction
basins and a general framework that would unite all different definitions/implementations used
for attraction basins. Conclusions: Findings regarding attraction basins in the field of metaheuristics
reveal that the concept alone is poorly exploited, as well as identify open issues where researchers
may improve their research.
Keywords: attraction basin, systematic mapping study, systematic review, metaheuristics
Published in DKUM: 16.06.2025; Views: 0; Downloads: 0
 Full text (5,97 MB)
This document has many files! More... |
2. An analysis of exploration and exploitation using attraction basins on 2D and 3D continuous functions : master's thesisMihael Baketarić, 2020, master's thesis Abstract: In this thesis we were discussing an analysis of numerical optimization algorithms from the most important aspect, that is exploration and exploitation. We focused on 2-dimensional and 3-dimensional unconstrained continuous functions, which were used to test the recently proposed metric based on attraction basins. The metric does not need any user-defined parameters. Attraction basins were expounded more profoundly and extensively. Our algorithm to calculate them consists of three steps such as making potential boundaries, filling, and then removing false boundaries from attraction basins. Results show that our algorithm is barely satisfying, depends on a particular problem function used. For example, attraction basins from Rastrigin, Schwefel, Ackley and similar functions (including all unimodal ones) were calculated accurately, while more special functions like Michalewicz, Shubert and Branin were proved to be not so easy. Further, we arbitrarly selected two algorithms, Particle Swarm Optimization and Self-adapting Differential Evolution, not for comparative study, rather to test the metric based on attraction basins. Results implied the relevance of recently proposed metric, and opened us a fruitful field for further investigation.
Keywords: exploration, exploitation, attraction basins, optimization, metaheuristic
Published in DKUM: 04.11.2020; Views: 889; Downloads: 95
Full text (1,76 MB) |
3. Prepoznavanje aktivnosti osebe iz zaporedja slik s pomočjo konvolucijskih nevronskih mrežMihael Baketarić, 2018, undergraduate thesis Abstract: V diplomskem delu smo se ukvarjali s prepoznavanjem aktivnosti osebe iz zaporedja slik. Omejili smo se na aktivnosti: stoji, sedi, leži, hitro hodi, počasi hodi in pada. Pregledali smo obstoječe postopke prepoznavanja, pripravili množico podatkov, preučili konvolucijske nevronske mreže in jih uporabili pri reševanju našega problema. Naš algoritem je sestavljen iz dveh korakov: iz izločevanja oseb iz slik in prepoznavanja aktivnosti. Oba koraka smo implementirali z uporabo konvolucijskih nevronskih mrež in analizirali rezultate. Za učenje in testiranje smo uporabili lastno podatkovno zbirko, ki je vsebovala video posnetke 6-ih različnih oseb, ki so izvajali vseh šest aktivnosti. Na veliko slikah oseba ni bila pravilno izločena oz. detektirana, zato se je naša množica podatkov občutno zmanjšala po odstranitvi takšnih slik. Naš postopek smo preverili s 6-kratno navzkrižno validacijo. Povprečna uspešnost prepoznavanja aktivnosti je bila 36 %, kar seveda ni dovolj visoko za realne aplikacije. Ugotavljamo, da se pri rezultatih prepoznavanja aktivnosti močno pozna dejstvo, da v našem postopku nismo upoštevali časovne komponente oz. rezultatov prepoznav na predhodnih slikah.
Keywords: računalniški vid, konvolucijska nevronska mreža, globoko učenje, detekcija oseb, prepoznavanje aktivnosti osebe
Published in DKUM: 19.10.2018; Views: 2091; Downloads: 269
Full text (1,55 MB) |
