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Representation Techniques for Evolutionary Design of Cellular Automata
Kovács, Martin ; Drábek, Vladimír (referee) ; Bidlo, Michal (advisor)
The aim of this thesis is to experimentally evaluate the performance of several distinct representations of transition functions for cellular automata. Cellular automata have many potential applications for simulating various phenomena (e.g. natural processes, physical systems, etc.). Parallel computation of cellular automata is based on local cell interactions. Such computation, however, may prove difficult to program the CA, which is the reason for applying evolutionary techniques for the design of cellular automata in many cases. Evolutionary algorithms, based on Darwin's theory of evolution, have been used to find human-competitive solutions to many problems. In order to perform the evolutionary design of cellular automata, special encodings of the candidate solutions are often necessary. For this purpose the performance testing of various representations of the transition functions will be investigated. In particular, table representation, conditionally matching rules, and genetic programming will be treated. The problem of square calculations in cellular automata will be considered as a case study.
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Cellular Automata Design Optimization
Jílek, Tomáš ; Drábek, Vladimír (referee) ; Bidlo, Michal (advisor)
Focus of this master's thesis is on evolutionary design of cellular automata and it's optimalization. There are described Evolutionary algorithms and Cellular automata in first part. Thereafter, one of the new ways of transition function representation and its possible evolutionary design is presented. Name of this method is Conditionally Matching Rules. This is followed by description of realized experiments with detailed results. Finally, success with optimalization for some tasks is presented in last chapter along with discussion.
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Modified Genetic Algorithms for Cellular Automata Design
Magdolen, Matej ; Vašíček, Zdeněk (referee) ; Bidlo, Michal (advisor)
This bachelor’s thesis aims to examine possibilities of designing a transition function enabling a cellular automaton to solve a given problem using a genetic algorithm. It contains an introduction to cellular automata, evolution algorithms and conditionally matching rules, a method of descripting a transition function suitable for evolutionary development. A set of experiments is conducted using the standard version of genetic algorithms to determine its optimal configuration. Additionally, modifications of this standard algorithm are proposed, effect of which on the algorithm’s performance is then evaluated by further experiments.
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Representation Techniques for Evolutionary Design of Cellular Automata
Kovács, Martin ; Drábek, Vladimír (referee) ; Bidlo, Michal (advisor)
The aim of this thesis is to experimentally evaluate the performance of several distinct representations of transition functions for cellular automata. Cellular automata have many potential applications for simulating various phenomena (e.g. natural processes, physical systems, etc.). Parallel computation of cellular automata is based on local cell interactions. Such computation, however, may prove difficult to program the CA, which is the reason for applying evolutionary techniques for the design of cellular automata in many cases. Evolutionary algorithms, based on Darwin's theory of evolution, have been used to find human-competitive solutions to many problems. In order to perform the evolutionary design of cellular automata, special encodings of the candidate solutions are often necessary. For this purpose the performance testing of various representations of the transition functions will be investigated. In particular, table representation, conditionally matching rules, and genetic programming will be treated. The problem of square calculations in cellular automata will be considered as a case study.
|
|
|
Cellular Automata Design Optimization
Jílek, Tomáš ; Drábek, Vladimír (referee) ; Bidlo, Michal (advisor)
Focus of this master's thesis is on evolutionary design of cellular automata and it's optimalization. There are described Evolutionary algorithms and Cellular automata in first part. Thereafter, one of the new ways of transition function representation and its possible evolutionary design is presented. Name of this method is Conditionally Matching Rules. This is followed by description of realized experiments with detailed results. Finally, success with optimalization for some tasks is presented in last chapter along with discussion.
|
|
|
Modified Genetic Algorithms for Cellular Automata Design
Magdolen, Matej ; Vašíček, Zdeněk (referee) ; Bidlo, Michal (advisor)
This bachelor’s thesis aims to examine possibilities of designing a transition function enabling a cellular automaton to solve a given problem using a genetic algorithm. It contains an introduction to cellular automata, evolution algorithms and conditionally matching rules, a method of descripting a transition function suitable for evolutionary development. A set of experiments is conducted using the standard version of genetic algorithms to determine its optimal configuration. Additionally, modifications of this standard algorithm are proposed, effect of which on the algorithm’s performance is then evaluated by further experiments.
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