Spelling suggestions: "subject:"coevolutionary algorithm"" "subject:"coevolutionary dalgorithm""
1 |
Koevoluce obrazových filtrů a detektorů šumu / Coevolution of Image Filters and Noise DetectorsKomjáthy, Gergely January 2014 (has links)
This thesis deals with image filter design using coevolutionary algorithms. It contains a description of evolutionary algorithms, focusing on genetic programming, cartesian genetic programming and coevolution, the reader can learn about image filters too. The next chapters contain the design of image filters and noise detectors using cooperative coevolution, and the implementation and testing of the proposed filter. In the last chapter the proposed filter is compared to other filters created using evolutionary algorithms but without coevolution.
|
2 |
Two Player Zero Sum Multi-Stage Game Analysis Using Coevolutionary AlgorithmNagrale, Sumedh Sopan 17 May 2019 (has links)
No description available.
|
3 |
Koevoluční algoritmy a klasifikace / Coevolutionary Algorithms and ClassificationHurta, Martin January 2021 (has links)
The aim of this work is to automatically design a program that is able to detect dyskinetic movement features in the measured patient's movement data. The program will be developed using Cartesian genetic programming equipped with coevolution of fitness predictors. This type of coevolution allows to speed up a design performed by Cartesian genetic programming by evaluating a quality of candidate solutions using only a part of training data. Evolved classifier achieves a performance (in terms of AUC) that is comparable with the existing solution while achieving threefold acceleration of the learning process compared to the variant without the fitness predictors, in average. Experiments with crossover methods for fitness predictors haven't shown a significant difference between investigated methods. However, interesting results were obtained while investigating integer data types that are more suitable for implementation in hardware. Using an unsigned eight-bit data type (uint8_t) we've achieved not only comparable classification performance (for significant dyskinesia AUC = 0.93 the same as for the existing solutions), with improved AUC for walking patient's data (AUC = 0.80, while existing solutions AUC = 0.73), but also nine times speedup of the design process compared to the approach without fitness predictors employing the float data type, in average.
|
Page generated in 0.0693 seconds