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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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Multiobjective Cartesian Genetic Programming
Petrlík, Jiří ; Schwarz, Josef (referee) ; Sekanina, Lukáš (advisor)
The aim of this diploma thesis is to survey the area of multiobjective genetic algorithms and cartesian genetic programming. In detail the NSGAII algorithm and integration of multiobjective optimalization into cartesian genetic programming are described. The method of multiobjective CGP was tested on selected problems from the area of digital circuit design.
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Development of Operating System Based on Evolutionary and Genetic Algorithms
Skorkovský, Petr ; Moučka,, Jiří (referee) ; Kovár, Martin (referee) ; Chvalina, Jan (advisor)
The main goal of the work is to introduce new ideas how traditional approaches for designing an operation system and associated software can be improved to be a part of automatic software evolution. It is generally supposed that algorithms found by the genetic programming processes cannot be used for exact calculations but only for approximate solutions. Several examples of software evolution are introduced, to show that quite precise solutions can be achieved. To reach this goal, characteristics of tree-like structures with approaches based on cellular automata features are combined in a new promising technique of algorithm representation, joining benefits of both concepts. An application has been developed based on these new genetic programming concepts and it is supposed it can be a part of a future automatic software evolution process.
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A Tool for Visual Analysis of Circuit Evolution
Staurovská, Jana ; Minařík, Miloš (referee) ; Sekanina, Lukáš (advisor)
The main goal of the master's thesis is to compose a study on cartesian genetic programming with focus on evolution of circuits and to design a concept for visualisation of this evolution. Another goal is to create a program to visualise the circuit evolution in cartesian genetic programming, its generations and chromosomes. The program is capable of visualising the changes between generations and chromosomes and comparing more chromosomes at once. Several user cases had been prepared for the resulting program.
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Evolutionary Design Method of Multipliers Using Development
Kaplan, Tomáš ; Jaroš, Jiří (referee) ; Bidlo, Michal (advisor)
This work is focused on the techniques for overcoming the problem of scale in the evolutionary design of the combinational multipliers. The approaches to the evolutionary design that work directly with the target solutions are not suitable for the design of the large-scale structures. An approach based on the biological principles of development has often been utilized as a non-trivial genotypephenotype mapping in the evolutionary algorithms that allows us to design scalable structures. The instruction-based developmental approach has been applied to the evolutionary design of generic circuit structures. In this work, three methods are presented for the construction of the combinational multipliers which use a ripple-carry adder for obtaining the final product.
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Comparison of Genetic Programming Variants in the Symbolic Regression Task
Doležal, Petr ; Hurta, Martin (referee) ; Drahošová, Michaela (advisor)
This thesis deals with comparison of genetic programming variants it the task of symbolic regression. Time to converge and quality of evolved solutions are evaluated on nine chosen benchmarks. In particular, tree-based genetic programming, cartesian genetic programming and their modifications using coevolutionary algorithm are investigated. An own implementation of employed methods (without a specific library use) allows to share as much code as possible. Moreover, an analysis of implemented methods efficiency on real world data is provided. Experimental results show that all of the investigated approaches are capable of finding solutions using symbolic regression. Cartesian genetic programming enhanced with coevolution seems to be the most suitable of the investigated approaches in terms of evolved solution quality and time to converge.
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Genetic programming - Java implementation
Tomaštík, Marek ; Kuba,, Martin (referee) ; Matoušek, Radomil (advisor)
This Master´s thesis implements computer program in Java, useful for automatic model generating, specially in symbolic regression problem. Thesis includes short description of genetic programming (GP) and own implementation with advanced GP operands (non-destructive operations, elitism, exptression reduction). Mathematical model is generating by symbolic regression, exacly for choosen data set. For functioning check are used test tasks. Optimal settings is found for choosen GP parameters.
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High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
Karásek, Jan ; Rakús,, Martin (referee) ; Cvrk, Lubomír (referee) ; Burget, Radim (advisor)
Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.
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Coevolutionary Algorithms and Classification
Hurta, Martin ; Sekanina, Lukáš (referee) ; Drahošová, Michaela (advisor)
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.
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