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Grammatical Evolution in Software Optimization
Pečínka, Zdeněk ; Minařík, Miloš (referee) ; Sekanina, Lukáš (advisor)
This master's thesis offers a brief introduction to evolutionary computation. It describes and compares the genetic programming and grammar based genetic programming and their potential use in automatic software repair. It studies possible applications of grammar based genetic programming on automatic software repair. Grammar based genetic programming is then used in design and implementation of a new method for automatic software repair. Experimental evaluation of the implemented automatic repair was performed on set of test programs.
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Evolutionary Optimization of Control Algorithms
Weisser, Roman ; Šeda, Miloš (referee) ; Zelinka,, Ivan (referee) ; Ošmera, Pavel (advisor)
The dissertation thesis deals with Evolution optimization of control algorithms. The first part of the thesis describes the principles and partial methods of evolution optimization methods especially those used in two-level transplant evolution method. Later the grammatical evolution method is described, which modified algorithm became impulse for creation of transplant evolution method. The transplant evolution method and its two-level modification are new evolutionary algorithms proposed in this work, which were used for optimization of structure and parameters of general controllers control algorithms. The transplant evolution algorithm and its extended two-level modification are described in detail in next chapters. The proper settings of evolutionary algorithms are important for minimization the time of optimization and for finds results approaching the global optimum. For proper setting the parameters of differential evolution was created meta-evolution algorithm that is described in chapter named meta-evolution. The basic concepts of control, chosen methods of system identification and controller parameters settings are described in next part. This part describes algorithms of digital controllers and some specific methods uses in digital control. The demonstrations of control algorithm optimizations of various types of controllers are showed in experimental part. The optimized algorithms of general controllers are compared with various types of PSD controllers which were set by various algebraic methods or differential evolution for various models of systems. In the conclusion of this work is stated a recommendation for further development of evolutionary optimization of controllers are focusing on parallel and distributed computing.
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Grammatical Evolution - Java
Bezděk, Pavel ; Kuba,, Martin (referee) ; Matoušek, Radomil (advisor)
The object of my thesis is the realization of grammatical evolution in the Java programming language for solving problems of approximation of functions and synthesis of logical circuits. The application is practical used for testing and gathering data in context of using different purpose function and parallel grammatical evolution. The data are analyzed and evaluated.
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Implementation of Grammatical Evolution System
Svoboda, Jan ; Hrbáček, Radek (referee) ; Sekanina, Lukáš (advisor)
Gramatická evoluce je relavitně nový přístup ke genetickému programování, který dokáže automatizovaně řešit různé problémy vytvářením programů v libovolném programovacím jazyce. Tato práce shrnuje prinicipy a algoritmy gramatické evoluce a poskytuje přehled o existujících systémech. Byla vytvořena nová knihovna Gram, která nabízí vysoký výkon a dodržuje dobré programátorské zvyklosti, jakými jsou modulárnost a automatické testování. Porovnání tohoto systému s nejvýkonnějším dostupným řešením ukázalo zlepšení v době výpočtu překračující 30 %. Gram byl také úspěšně použit pro automatizaci testy řízeného vývoje, techniky běžně používané při vytváření softwaru s automatizovanými testy. Tato práce a doplňující softwarový projekt tedy poskytují solidní základ pro další výzkum a umožňují využití gramatické evoluce v nových oblastech.
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Grammar-based genetic programming
Nohejl, Adam ; Mráz, František (advisor) ; Iša, Jiří (referee)
Tree-based genetic programming (GP) has several known shortcomings: difficult adaptability to specific programming languages and environments, the problem of closure and multiple types, and the problem of declarative representation of knowledge. Most of the methods that try to solve these problems are based on formal grammars. The precise effect of their distinctive features is often difficult to analyse and a good comparison of performance in specific problems is missing. This thesis reviews three grammar-based methods: context-free grammar genetic programming (CFG-GP), including its variant GPHH recently applied to exam timetabling, grammatical evolution (GE), and LOGENPRO, it discusses how they solve the problems encountered by GP, and compares them in a series of experiments in six applications using success rates and derivation tree characteristics. The thesis demonstrates that neither GE nor LOGENPRO provide a substantial advantage over CFG-GP in any of the experiments, and analyses the differences between the effects of operators used in CFG-GP and GE. It also presents results from a highly efficient implementation of CFG-GP and GE.
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