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Co-Learning in Cartesian Genetic Programming
Korgo, Jakub ; Grochol, David (referee) ; Wiglasz, Michal (advisor)
This thesis deals with the integration of co-learning into cartesian genetic programming. The task of symbolic regression was already solved by cartesian genetic programming, but this method is not perfect yet. It is relatively slow and for certain tasks it tends not to find the desired result. However with co-learning we can enhance some of these attributes. In this project we introduce a genotype plasticity, which is based on Baldwins effect. This approach allows us to change the phenotype of an individual while generation is running. Co-learning algorithms were tested on five different symbolic regression tasks. The best enhancement delivered in experiments by co-learning was that the speed of finding a result was 15 times faster compared to the algorithm without co-learning.
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Accelerated Linear Genetic Programming in Hardware
Ťupa, Josef ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
The aim of this thesis is to design and implement hardware acceleration of linear genetic programming for symbolic regression. The thesis contains a theoretical introduction into the studies of modern hardware and genetic programming design. Design and implementation of the LGP for symbolic regression is described in the rest of the thesis.
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Genetic Improvement of Cartesian Genetic Programming Software
Husa, Jakub ; Jaroš, Jiří (referee) ; Sekanina, Lukáš (advisor)
Genetic programming is a nature-inspired method of programming that allows an automated creation and adaptation of programs. For nearly two decades, this method has been able to provide human-comparable results across many fields. This work gives an introduction to the problems of evolutionary algorithms, genetic programming and the way they can be used to improve already existing software. This work then proposes a program able to use these methods to improve an implementation of cartesian genetic programming (CGP). This program is then tested on a CGP implementation created specifically for this project, and its functionality is then verified on other already existing implementations of CGP.
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Co-Learning in Cartesian Genetic Programming
Korgo, Jakub ; Grochol, David (referee) ; Wiglasz, Michal (advisor)
This thesis deals with the integration of co-learning into cartesian genetic programming. The task of symbolic regression was already solved by cartesian genetic programming, but this method is not perfect yet. It is relatively slow and for certain tasks it tends not to find the desired result. However with co-learning we can enhance some of these attributes. In this project we introduce a genotype plasticity, which is based on Baldwins effect. This approach allows us to change the phenotype of an individual while generation is running. Co-learning algorithms were tested on five different symbolic regression tasks. The best enhancement delivered in experiments by co-learning was that the speed of finding a result was 15 times faster compared to the algorithm without co-learning.
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|
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Genetic Improvement of Cartesian Genetic Programming Software
Husa, Jakub ; Jaroš, Jiří (referee) ; Sekanina, Lukáš (advisor)
Genetic programming is a nature-inspired method of programming that allows an automated creation and adaptation of programs. For nearly two decades, this method has been able to provide human-comparable results across many fields. This work gives an introduction to the problems of evolutionary algorithms, genetic programming and the way they can be used to improve already existing software. This work then proposes a program able to use these methods to improve an implementation of cartesian genetic programming (CGP). This program is then tested on a CGP implementation created specifically for this project, and its functionality is then verified on other already existing implementations of CGP.
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|
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Accelerated Linear Genetic Programming in Hardware
Ťupa, Josef ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
The aim of this thesis is to design and implement hardware acceleration of linear genetic programming for symbolic regression. The thesis contains a theoretical introduction into the studies of modern hardware and genetic programming design. Design and implementation of the LGP for symbolic regression is described in the rest of the thesis.
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