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Boosting and Evolution
Mrnuštík, Michal ; Juránek, Roman (referee) ; Hradiš, Michal (advisor)
This thesis introduces combination of the AdaBoost and the evolutionary algorithm. The evolutionary algorithm is used to find linear combination of Haar features. This linear combination creates the feature to train weak classifier for AdaBoost. There are described basics of classification, Haar features and the AdaBoost. Next there are basic information about evolutionary algorithms. Theoretical description of combination of the AdaBoost and the evolutionary algorithm is included too. Some implementation details are added too. Implementation is tested on the images as part of the system for face recognition. Results are compared with Haar features.
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Intelligent Web Work Planner
Kmeť, Miroslav ; Vrábel, Lukáš (referee) ; Čermák, Martin (advisor)
This thesis describes basic principles governing the use of evolutionary algorithms. Thesis deals with the usage of the evolutionary algorithms for scheduling the work between group of employees. Genetic algorithms, which represents intelligent stochastic optimization techniques based on the mechanism of natural selection and genetics are mainly used to solve this problem. Each solution is represented as an individual in population and only the most adapted ones are selected for the process of reproduction.
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Evolutionary Solving of the Rubik's Cube
Mališ, Radim ; Sekanina, Lukáš (referee) ; Jaroš, Jiří (advisor)
This thesis deals with an evolutionary solving of the Rubik's cube. The worldwide known puzzle has been for several decades not only a toy for children and adults, but also almost a lifestyle for crowds of fans and definitely a big challenge in the world of computation, where scientists seek to find an effective automated solution. The potential for its solution could also be borne by evolutionary algorithms. The author of this thesis has developed an application employing, apart from genetic algorithms, also many advanced technics, such as linear genetic programming or local search. The goal of this special technics is to make the evolutionary process more effective. There have also been made tests of the crossover, the population size and the mutation probability influence. All the tests have been statistically evaluated.
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Symbolic Regression and Coevolution
Drahošová, Michaela ; Žaloudek, Luděk (referee) ; Sekanina, Lukáš (advisor)
Symbolic regression is the problem of identifying the mathematic description of a hidden system from experimental data. Symbolic regression is closely related to general machine learning. This work deals with symbolic regression and its solution based on the principle of genetic programming and coevolution. Genetic programming is the evolution based machine learning method, which automaticaly generates whole programs in the given programming language. Coevolution of fitness predictors is the optimalization method of the fitness modelling that reduces the fitness evaluation cost and frequency, while maintainig evolutionary progress. This work deals with concept and implementation of the solution of symbolic regression using coevolution of fitness predictors, and its comparison to a solution without coevolution. Experiments were performed using cartesian genetic programming.
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Modularity in the Evolutionary Design
Klemšová, Jarmila ; Bidlo, Michal (referee) ; Vašíček, Zdeněk (advisor)
The diploma thesis deals with the evolutionary algorithms and their application in the area of digital circuit design. In the first part, general principles of evolutionary algorithms are introduced. This part includes also the introduction of genetic algorithms and genetic programming. The next chapter describes the cartesian genetic programming and its modifications like embedded, self-modifying or multi-chromosome cartessian genetic programming. Essential part of this work consists of the design and implementation of a modularization technique for evolution circuit design. The proposed approach is evaluated using a set of standard benchmark circuits.
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Learnable Evolution Model for Optimization (LEM)
Weiss, Martin ; Vašíček, Zdeněk (referee) ; Schwarz, Josef (advisor)
Numerical optimization of multimodal or otherwise nontrivial functions has stayed around the peak of the interest of many researchers for a long time. One of the promising methods that appeared is the hybrid approach of the Learnable Evolution Model that combines the well-established ways of artificial intelligence and machine learning with recently popular and efective methods of evolutionary programming. In this work, the method itself was reviewed with respect to what has been already implemented and tested and several possible new implementations of the method were proposed and some of them consequently implemented. The resulting program was then tested against a set of chosen nontrivial real-valued functions and its results were compared to those achieved with EDA algorithms.
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Optimisation of Exhaust Drains Shape
Navrátil, Dušan ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
Multiobjective optimization system of exhaust manifold shapes including initial design has been developed. Space of possible solutions is explored by an evolutionary algorithm. Evaluation of exhaust drains shape comes from drains length and sum of arc angles. Drains mustn't interfere in surrounding parts. System is tested on set of input data originated from practice. Further, performance of proposed evolutionary algorithm is evaluated.
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Evolutionary Optimization of Tour Timetables
Filák, Jakub ; Bidlo, Michal (referee) ; Jaroš, Jiří (advisor)
This thesis deals with the problem of vehicle scheduling in public transport. It contains a theoretical introduction to vehicles scheduling and evolutionary algorithms. Vehicle scheduling is analyzed with respect to the bus timetables. Analysis of evolutionary algorithms is done with emphasis on the genetic algorithms and tabu-search method After the theoretical introduction, a memetic algorithm for the given problem is analyzed. Finally, the thesis contains a description of the optimization system implementation and discusses the experiments with the system.
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Evolutionary Design of 3D Structures
Kovařík, Roman ; Sekanina, Lukáš (referee) ; Jaroš, Jiří (advisor)
This work deals with evolutionary design of 3D structures. The work brings the summary of the previous works in this area and brings autor's suggested solution of evolutionary design of 3D structures. This paper seeks to the ability of easy fitness function definition in the systems for evolutionary design of structures. The author tries to make one of the first steps to the future systems for evolution design of any universal structures in contrast with the evolution systems for design of a concrete type of structure. The result of this work is the basic system for evolutionary design of 3D structures with the ability of external fitness function definition via the XML file. This paper offers also the simple advices and observations for the potential future work in this area.
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