|
|
Neural Networks Classifier Design using Genetic Algorithm
Tomášek, Michal ; Vašíček, Zdeněk (referee) ; Mrázek, Vojtěch (advisor)
The aim of this work is the genetic design of neural networks, which are able to classify within various classification tasks. In order to create these neural networks, algorithm called NeuroEvolution of Augmenting Topologies (also known as NEAT) is used. Also the idea of preprocessing, which is included in implemented result, is proposed. The goal of preprocessing is to reduce the computational requirements for processing of benchmark datasets for classification accuracy. The result of this work is a set of experiments conducted over a data set for cancer cells detection and a database of handwritten digits MNIST. Classifiers generated for the cancer cells exhibits over 99 % accuracy and in experiment MNIST reduces computational requirements more than 10 % with bringing negligible error of size 0.17 %.
|
|
|
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.
|
|
|
Řízení autonomního agenta pomocí neuroevoluce
Hnátek, Martin
Thesis describe theory behind neuroevolution. Then it describes both design and creation of simulated environment for autonomous agent and its training with library Neataptic in environments with various difficulty. Thesis also describes pro- cess of designing frontend for visualization of results and backend for faster training of agents. At the end it describes resulting agents and proposes enhancements to existing solution.
|
|
|
Neural Networks Classifier Design using Genetic Algorithm
Tomášek, Michal ; Vašíček, Zdeněk (referee) ; Mrázek, Vojtěch (advisor)
The aim of this work is the genetic design of neural networks, which are able to classify within various classification tasks. In order to create these neural networks, algorithm called NeuroEvolution of Augmenting Topologies (also known as NEAT) is used. Also the idea of preprocessing, which is included in implemented result, is proposed. The goal of preprocessing is to reduce the computational requirements for processing of benchmark datasets for classification accuracy. The result of this work is a set of experiments conducted over a data set for cancer cells detection and a database of handwritten digits MNIST. Classifiers generated for the cancer cells exhibits over 99 % accuracy and in experiment MNIST reduces computational requirements more than 10 % with bringing negligible error of size 0.17 %.
|
|
|
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.
|
|
|
Freight in Alps and Switzerland
Vonásek, Martin ; Zelený, Lubomír (advisor) ; Pešková, Marie (referee)
The paper deals with extarnalities in transport, position of Switzerland in european freight and situation in alpine freight in overall. Analyzes instruments applied in the swiss Road to rail transfer politics, infrastructure charging, rail infrastructure projects and development of freight in swiss alpine tunnels since 1980. The paper briefly discuses possible solitions with overlap into future of swiss trafic management projects.
|
guest ::
