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Evolutionary Algorithms for 2D Cutting Problem
Balcar, Štěpán ; Pilát, Martin (advisor) ; Mareš, Martin (referee)
Creation of optimal cutting plans is an important task in many types of industry. In this work we present a novel evolutionary algorithm designed to deal with this problem. The algorithm assumes rectangular shapes of the objects and creates a cutting plan which is can be cut out using a circular saw. The output is presented in a form usable by automatic saws as well as graphically. The algorithm reduces the amount of the material used and, moreover, also reduces the number of needed employees.
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Machine Learning in the Monitoring of Computer Clusters
Adam, Martin ; Pilát, Martin (advisor) ; Balcar, Štěpán (referee)
With the explosion of the number of distributed applications, a new dynamic server environment emerged grouping servers into clusters, whose utilization depends on the cur- rent demand for the application. Detecting and fixing erratic server behavior is paramount for providing maximal service stability and availability. Using standard techniques to de- tect such behavior is yielding sub-optimal results. We have collected a dataset of OS-level performance metrics from a cluster running a streaming distributed application and in- jected artificially created anomalies. We then selected a set of various machine learning algorithms and trained them for anomaly detection on said dataset. We evaluated the algorithms performance and proposed a system for generating notifications of possible erratic behavior, based on the analysis of the best performing algorithm. 1
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Heterogeneous Island Models
Balcar, Štěpán ; Pilát, Martin (advisor)
The work deals with heterogeneous island models. The work designs and implements a new island model based on knowledge of homogeneous models of evolutionary algorithms. The model allows dynamic replanning of general computational methods. The work experimentally compares results of homogeneous and heterogeneous models.
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Heterogeneous Island Models
Balcar, Štěpán ; Pilát, Martin (advisor)
The work deals with heterogeneous island models. The work designs and implements a new island model based on knowledge of homogeneous models of evolutionary algorithms. The model allows dynamic replanning of general computational methods. The work experimentally compares results of homogeneous and heterogeneous models.
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Deep Learning For Implicit Feedback-based Recommender Systems
Yöş, Kaan ; Peška, Ladislav (advisor) ; Balcar, Štěpán (referee)
The research aims to focus on Recurrent Neural Networks (RNN) and its application to the session-aware recommendations empowered by implicit user feedback and content-based metadata. To investigate the promising architecture of RNN, we implement seven different models utilizing various types of implicit feedback and content information. Our results showed that using RNN with complex implicit feedback increases the next-item prediction comparing the baseline models like Cosine Similarity, Doc2Vec, and Item2Vec.
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Machine Learning in the Monitoring of Computer Clusters
Adam, Martin ; Pilát, Martin (advisor) ; Balcar, Štěpán (referee)
With the explosion of the number of distributed applications, a new dynamic server environment emerged grouping servers into clusters, whose utilization depends on the cur- rent demand for the application. Detecting and fixing erratic server behavior is paramount for providing maximal service stability and availability. Using standard techniques to de- tect such behavior is yielding sub-optimal results. We have collected a dataset of OS-level performance metrics from a cluster running a streaming distributed application and in- jected artificially created anomalies. We then selected a set of various machine learning algorithms and trained them for anomaly detection on said dataset. We evaluated the algorithms performance and proposed a system for generating notifications of possible erratic behavior, based on the analysis of the best performing algorithm. 1
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Framework Supporting Online Evaluation of Recommender Systems
Novák, Ondřej ; Peška, Ladislav (advisor) ; Balcar, Štěpán (referee)
This work aims to highlight the importance of online evaluation for the testing of recommender systems. Firstly, we will look at the methods and the ways modern recommender systems operate. We will also introduce how they are compared both in online and offline settings. With this knowledge, we aim to build a .NET framework capable of tracking the various recommender systems for the purpose of measuring and comparing their performance during online use. To showcase the functionality of this framework, we use it to create a mock-up of an online movie database, where users can rate movies and receive movie recommendations.
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Similarity methods for music recommender systems
Vystrčilová, Michaela ; Peška, Ladislav (advisor) ; Balcar, Štěpán (referee)
Traditional music recommender systems rely on collaborative-filtering methods. This, however, puts listeners who do not enjoy mainstream songs at a disadvantage because CF systems depend on popularity patterns. Content-based recommendation methods might be useful in solving this issue. Since tag-based searches are a widespread tool to aid tra- ditional music recommendation, this paper presents content-based methods measuring similarity between songs with focus on methods utilizing song's lyrics and audio record- ings. First, we evaluated the accuracy of several approaches based on lyrics and audio information on real user playlists and found that lyrics-based methods yield competitive results to audio-based methods. Results also revealed that both categories include meth- ods that are 100 times more accurate compared to random suggestions and that they have potential for even better results. After the evaluation phase, we selected well-performing methods and implemented them in a web application aiming on recommending novel music to the users based on their content-based profile. 1
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Evolutionary Algorithms for the Control of Heterogeneous Robotic Swarms
Karella, Tomáš ; Pilát, Martin (advisor) ; Balcar, Štěpán (referee)
Robotic swarms are often used for solving different tasks. Many articles are focused on generating robot controllers for swarm behaviour using evolutionary algorithms. Most of them are nevertheless considering only homogenous robots. The goal of this thesis is to use evolutionary algorithms for behaviours of heterogeneous robotic swarms. A 2D simulation was implemented to explore swarm controller optimization methods with the ability to create custom scenarios for robotic swarms. We tested differential evolution and evolution strategies on three different scenarios.
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Heterogeneous Island Models
Balcar, Štěpán ; Pilát, Martin (advisor) ; Matzner, Filip (referee)
The work deals with heterogeneous island models. The work designs and implements a new island model based on knowledge of homogeneous models of evolutionary algorithms. The model allows dynamic replanning of general computational methods. The work experimentally compares results of homogeneous and heterogeneous models.
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