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Neural Networks and Genetic Algorithm
Karásek, Štěpán ; Snášelová, Petra (referee) ; Zbořil, František (advisor)
This thesis deals with evolutionary and genetic algorithms and the possible ways of combining them. The theoretical part of the thesis describes genetic algorithms and neural networks. In addition, the possible combinations and existing algorithms are presented. The practical part of this thesis describes the implementation of the algorithm NEAT and the experiments performed. A combination with differential evolution is proposed and tested. Lastly, NEAT is compared to the algorithms backpropagation (for feed-forward neural networks) and backpropagation through time (for recurrent neural networks), which are used for learning neural networks. Comparison is aimed at learning speed, network response quality and their dependence on network size.
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Evolutionary algorithms for global optimization problem solving
Dragon, Ondřej ; Kozumplík, Jiří (referee) ; Mézl, Martin (advisor)
This work is devoded to evolutionary algorithms and solution of global optimization problems, mainly the traveling salesman problem. The traveling salesman problem is analyzed in detail as well as its methods of solution, such as: graph theory, heuristics and evolutionary algorithms. The main optimization method of this work is a Inver - over operator. In conclusion are implemented selected methods and performed testing and evaluation of the individual data sets.
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Evolution algorithms in network elements
Braciník, Roman ; Sobek, Jiří (referee) ; Vychodil, Petr (advisor)
This semestral project deals wtih genetic algorithm issues and its utilization in network elements. The author analyses basic and improved genetic algorithm methods. The different way of adaptation of the algorithm on certain issues and their errors in usage will be discused further. In the conclusion the author discusses about the possible use of genetic algorithms in network elements.
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Evolution algorithms in network elements
Braciník, Roman ; Sobek, Jiří (referee) ; Vychodil, Petr (advisor)
This semestral project deals wtih genetic algorithm issues and its utilization in network elements. The author analyses basic and improved genetic algorithm methods. The different way of adaptation of the algorithm on certain issues and their errors in usage will be discused further. In the conclusion the author discusses about the possible use of genetic algorithms in network elements.
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Competitive Coevolution in Cartesian Genetic Programming
Skřivánková, Barbora ; Petrlík, Jiří (referee) ; Drahošová, Michaela (advisor)
Symbolic regression is a function formula search approach dealing with isolated points of the function in plane or space. In this thesis, the symbolic regression is performed by Cartesian Genetic Programming and Competitive Coevolution. This task has already been resolved by Cartesian Genetic Programming using Coevolution of Fitness Predictors. This thesis is concerned with comparison of Coevolution of Fitness Predictors with simpler Competitive Coevolution approach in terms of approach effort. Symbolic regression has been tested on five functions with different complexity. It has been shown, that Competitive Coevolution accelerates the symbolic regression task on plainer functions in comparison with Coevolution of Fitness Predictors. However, Competitive Coevolution is not able to solve more complex functions in which Coevolution of Fitness Predictors succeeded.
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Diffusion Evolutionary Algorithm
Mészáros, István ; Pospíchal, Petr (referee) ; Jaroš, Jiří (advisor)
There are new trends in artificial intelligence nowadays. Methods known as evolutionary algorithms are one of them. These algorithms allow us to design and optimize systems using computers. One of the variants of evolutionary algorithms is the diffusion evolutionary algorithm. This type of algorithms is able to run in parallel, and besides that it brings many positive features. The question is under what conditions the diffusion variant of evolutionary algorithms can effectively be used. Is it possible to use for planning systems and for problem optimization? Why are they more favorable than other types of evolutionary algorithms? This work tries to answer these questions and explain the behavior of these algorithms.
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The Impact of Candidate Solution Mappings on Evolutionary Algorithm Efficiency
Hrbáček, Jiří ; Korček, Pavol (referee) ; Křivánek, Jan (advisor)
The Concern of the present study is summarizing knowledges in the theory of mapping candidate solutions , analysis and application of evolutionary algorithms. The study provides summary of the evolutionary algorithms, classification and application. The target of the study is links gained knowledge from sectionS of ; evolutionary algorithms, mapping candidate solutions and creations of a system that will demonstrate and influence mapping the efficiency of the evolutionary algorithms succesfully.
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Diffusion Evolutionary Algorithm
Žundálek, Zbyněk ; Puš, Viktor (referee) ; Jaroš, Jiří (advisor)
This bachelor thesis deals with a parallelization of cellular evolutionary algorithms using OpenMP. The theoretical part of the thesis contains an introduction to evolutionary and genetic algorithms followed by the description of their parallel implementation on shared memory systems. This part is completed with the OpenMP key features analysis. The practical part of this thesis describes two possible implementations of a diffusion evolutionary algorithm; synchronous and asynchronous. The comparison of achievable performance of these two methods carried out on the N-Queen problem is provided in the experimental part of the thesis. The quality of found solutions is further examined with respect to the neighborhood size, topology and the replacement operator of the diffusion evolutionary algorithm.
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