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Artificial Bee Colony
Jukl, Jan ; Pangrác, Ondřej (advisor) ; Hušek, Radek (referee)
The minimum vertex cover (MVC) problem is a well-known NP-hard prob- lem. This thesis presents the Artificial Bee Colony (ABC) algorithm and two genetic algorithm approaches to solve this problem. The ABC algorithm is an optimization algorithm based on the intelligent behaviour of a honey bee swarm. It was first proposed for unconstrained optimization problems and showed that it is superior in performance on these kinds of problems. In this thesis, the ABC algorithm has been extended to solving the minimum vertex cover problem and applied to DIMACS and BHOSLIB benchmarks. The results produced by the ABC, the binary decision diagram based genetic algorithm and the MVC-aware genetic algorithm have been compared.
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Discretization Of Decision Variables In
Optimization Algorithms
Marek, Martin
This paper presents a verification of universal method for discretization of decision space in optimization algorithms. Real-world optimization tasks frequently use discontinuous decision variables and in order to effectively optimize such tasks, it is necessary to exploit an optimization algorithm that meets such requirement. Unfortunately, very few evolutionary algorithms can naturally work with discontinuous decision space. The method that entitles all optimization algorithms to effectively solve problems with discrete variables is here described and experimentally verified.
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Layout optimisation method for active structural health monitoring
Červenka, Miroslav ; Koštial, Rostislav
This paper describes optimisation method for designing sensoric layout for Active Structural Health Monitoring (A-SHM) by Ultrasonic Guided Waves (UGW) on metal and non-metal (composite) materials. The SHM sensors need to be placed optimally in order to detect structural damage with high probability before the damage turns critical. Configuration of used optimisation algorithm for such task is not straightforward. Differential Evolution (DE) has two configuration parameters – the mutation factor F and the crossover rate CR – whose settings largely influence the solution quality the optimisation process can yield. For that matter we describe an elaborated a method to guide this selection towards good results using visual heat maps with the intent to select best DE’s variant and particular configuration to receive the most optimal SHM sensorics layout.
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Adversarial examples generation for deep neural networks
Procházka, Štěpán ; Neruda, Roman (advisor) ; Kratochvíl, Miroslav (referee)
Machine learning models exhibit vulnerability to adversarial examples i.e., artificially created inputs that become misinterpreted. The goal of this work is to explore black-box adversarial attacks on deep networks performing image classification. The role of surrogate machine learning models for adversarial attacks is studied, and a special version of a genetic algorithm for generating adversarial examples is proposed. The efficiency of attacks is validated by a multitude of experiments with the Fashion MNIST data set. The experimental results verify the usability of our approach with surprisingly good performance in several cases, such as non-targeted attack on residual networks.
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Evolutionary Algorithm-based Procedural Level Generator for a Rogue-like Game
Vegricht, Jan ; Gemrot, Jakub (advisor) ; Zelinka, Mikuláš (referee)
Title: Evolutionary Algorithm-Based Procedural Level Generator for a Rogue-like Game Author: Jan Vegricht Department: Department of Software and Computer Science Education Supervisor: Mgr. Jakub Gemrot, Ph.D., Department of Software and Computer Science Education Abstract: Rogue-like games are genre with long tradition in game industry. One significant factor commonly associated with this genre is procedural level generation. The goal of this thesis is to design and implement a level generator for one concrete rogue-like game using evolutionary algorithms as main means of generation. Methods and results are then compared to non-evolutionary alternative algorithms, attempting to generate comparable solutions. The results seem to indicate that while evolutionary algorithms can be used to generate dungeons, practicality of this approach is for the most part limited. Keywords: evolutionary algorithms, procedural generation, constrained optimization, rogue-like
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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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Generation of Vector Images using Evolutionary Algorithms
Drázdová, Zuzana ; Pilát, Martin (advisor) ; Křen, Tomáš (referee)
The usage of evolutionary algorithms for generating images has been researched for several decades now. The potential of this approach comes from the creative power of genetic operators and broad possibilities for automated evaluation of solutions. Individuals can be either evolved to resemble an existing image or other criteria such as artistic quality can be employed. Generating vector images to resemble raster models got a lot of attention in past years. It offers several benefits. Such images can be easily scaled without any loss of accuracy. Another advantage is the option to modify individual objects in an image separately. This aspect was, so far, being neglected. We want to reach full potential of evolved images by designing a suitable algorithm. Our method generates vector images similar to given raster model that are easily editable and have an interesting artistic overlap. We developed three techniques which differ in approach to individual representation, genetic operators, evaluation and overall style of results.
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Image Classification Using Genetic Programming
Jašíčková, Karolína ; Vašíček, Zdeněk (referee) ; Sekanina, Lukáš (advisor)
This thesis deals with image classification based on genetic programming and coevolution. Genetic programming algorithms make generating executable structures possible, which allows us to design solutions in form of programs. Using coevolution with the fitness prediction lowers the amount of time consumed by fitness evaluation and, therefore, also the execution time. The thesis describes a theoretical background of evolutionary algorithms and, in particular, cartesian genetic programming. We also describe coevolutionary algorithms properties and especially the proposed method for the image classifier evolution using coevolution of fitness predictors, where the objective is to find a good compromise between the classification accuracy, design time and classifier complexity. A part of the thesis is implementation of the proposed method, conducting the experiments and comparison of obtained results with other methods.
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Aplikace evolučního algoritmu na optimalizační úlohu vibračního generátoru
Nguyen, Manh Thanh ; Kovář, Jiří (referee) ; Hadaš, Zdeněk (advisor)
This thesis will deal with the use of artificial intelligence methods for solving optimization problems with multiple variables. A theorethical part presents problems of global optimization and overview of solution methods. For practical reasons, special attention is paid to evolutionary algorithms. The subject of optimization itself is energy harvester based on a piezoelectric effect. Its nature and modeling is devoted to one chapter. A part of the thesis is the implementation of the SOMA algorithm for finding the optimal parameters of the generator for maximum performance.
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General Artificial Intelligence for Game Playing
Klůj, Jan ; Pilát, Martin (advisor) ; Moudřík, Josef (referee)
Game playing is a relatively interesting task in the field of artificial intelligence in these days. The master thesis deals with general artificial intelligence which is capable of playing selected simple games based on information that is also avai- lable to the human player. Our selected games are 2048, Mario, racing simulator TORCS and Alhambra. All the information acquired by artificial intelligence is provided by games through an interface, therefore none of the models uses visual input. We use evolutionary approaches such as evolutionary algorithms, evolutio- nary strategy CMA and differential evolution applied to different types of neural networks. We are also dealing with deep reinforcement learning. We test these approaches and compare their results. 1
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