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1.
Maximum number of generations as a stopping criterion considered harmful
Miha Ravber, Shih-Hsi Liu, Marjan Mernik, Matej Črepinšek, 2022, izvirni znanstveni članek

Opis: Evolutionary algorithms have been shown to be very effective in solving complex optimization problems. This has driven the research community in the development of novel, even more efficient evolutionary algorithms. The newly proposed algorithms need to be evaluated and compared with existing state-of-the-art algorithms, usually by employing benchmarks. However, comparing evolutionary algorithms is a complicated task, which involves many factors that must be considered to ensure a fair and unbiased comparison. In this paper, we focus on the impact of stopping criteria in the comparison process. Their job is to stop the algorithms in such a way that each algorithm has a fair opportunity to solve the problem. Although they are not given much attention, they play a vital role in the comparison process. In the paper, we compared different stopping criteria with different settings, to show their impact on the comparison results. The results show that stopping criteria play a vital role in the comparison, as they can produce statistically significant differences in the rankings of evolutionary algorithms. The experiments have shown that in one case an algorithm consumed 50 times more evaluations in a single generation, giving it a considerable advantage when max gen was used as the stopping criterion, which puts the validity of most published work in question.
Ključne besede: evolutionary algorithms, stopping criteria, benchmarking, algorithm termination, algorithm comparison
Objavljeno v DKUM: 28.03.2025; Ogledov: 0; Prenosov: 2
.pdf Celotno besedilo (1,40 MB)
Gradivo ima več datotek! Več...

2.
Comparison and optimization of algorithms for simultaneous localization and mapping on a mobile robot : master's thesis
Matic Rašl, 2023, magistrsko delo

Opis: In this thesis, we compare and evaluate different SLAM solutions for a low-cost mobile robot. We present a simulator for the robot and use it to gather simulated data. Using this data, we then optimize the SLAM algorithms using an evolutionary algorithm. The optimized solutions are then validated and compared to default SLAM configurations. Up to 83 % reduction of error is achieved on validation data with multiple SLAM algorithms with improvements also visible on the real-world data.
Ključne besede: SLAM, optimization, mobile robot, evolutionary algorithm, simulation.
Objavljeno v DKUM: 05.10.2023; Ogledov: 376; Prenosov: 35
.pdf Celotno besedilo (2,64 MB)

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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: 3471; Prenosov: 336
.pdf Celotno besedilo (8,69 MB)

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Autonomous evolutionary algorithm
Matej Šprogar, 2010, samostojni znanstveni sestavek ali poglavje v monografski publikaciji

Ključne besede: algorithm, evolutionary algorithm, controlling evolution
Objavljeno v DKUM: 31.05.2012; Ogledov: 1702; Prenosov: 101
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