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Evolutionary Algorithms for Multiobjective Optimization
Pilát, Martin ; Neruda, Roman (advisor) ; Schoenauer, Marc (referee) ; Pošík, Petr (referee)
Multi-objective evolutionary algorithms have gained a lot of atten- tion in the recent years. They have proven to be among the best multi-objective optimizers and have been used in many industrial ap- plications. However, their usability is hindered by the large number of evaluations of the objective functions they require. These can be expensive when solving practical tasks. In order to reduce the num- ber of objective function evaluations, surrogate models can be used. These are a simple and fast approximations of the real objectives. In this work we present the results of research made between the years 2009 and 2013. We present a multi-objective evolutionary algo- rithm with aggregate surrogate model, its newer version, which also uses a surrogate model for the pre-selection of individuals. In the next part we discuss the problem of selection of a particular type of model. We show which characteristics of the various models are im- portant and desirable and provide a framework which combines sur- rogate modeling with meta-learning. Finally, in the last part, we ap- ply multi-objective optimization to the problem of hyper-parameters tuning. We show that additional objectives can make finding of good parameters for classifiers faster. 1
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Converged Networks and Traffic Tomography by Using Evolutionary Algorithms
Oujezský, Václav ; Sýkora, Jiří (referee) ; Polívka, Michal (referee) ; Škorpil, Vladislav (advisor)
Nowadays, the traffic tomography represents an integral component in converged networks and systems for detecting their behavioral characteristics. The dissertation deals with research of its implementation with the use of evolutionary algorithms. The research was mainly focused on innovation and solving behavioral detection data flows in networks and network anomalies using tomography and evolutionary algorithms. Within the dissertation has been proposed a new algorithm, emerging from the basics of the statistical method survival analysis, combined with a genetics’ algorithm. The proposed algorithm was tested in a model of a self-created network probe using the Python programming language and Cisco laboratory network devices. Performed tests have shown the basic functionality of the proposed solution.
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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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Study and comparison of main kinds of evolutionary algorithms
Štefan, Martin ; Holeňa, Martin (advisor) ; Gemrot, Jakub (referee)
Evolutionary algorithms belongs among the youngest and the most progressive methods of solving difficult optimization tasks. They received huge popularity mainly due to good experimental results in optimization, a simplicity of the implementation and a high modularity, which is an ability to be modified for different problems. Among the most frequently used Evolutionary algorithms belongs Genetic Algorithm, Differential Evolution and Evolutionary Strategy. It is able to apply these algorithms and theirs variants to both continuous, discrete and mixed optimization tasks. A subject of this theses is to compare three main types of algorithms on the catalyst optimization task with mixed variables, linear constraints and experimentally evaluated fitness function.
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Evolutionary development of robotic organisms
Leibl, Marek ; Mráz, František (advisor) ; Holan, Tomáš (referee)
This work introduces a system for an evolutionary design of virtual organisms capable of effective movement in a simulated environment. The morphology and the control system are simultaneously developed by an evolutionary algorithms. The system also allows to design organisms in an editor and evolution of the control system with an immutable morphology. The quality evaluation and viewing of evolved organisms is done in a simulated 3D physical environment. The work put stress on the optimization of time and computing complexity of the evolutionary process. This optimization is achieved by using symmetry of organisms and their movement with HyperNEAT-generative encoding of synaptic values. Further optimization is achieved by limiting the variety of mutual module connections and focusing on the harmonic movement of organisms.
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Evolutionary Algorithms for 2D Cutting Problem
Balcar, Štěpán ; Pilát, Martin (advisor) ; Mareš, Martin (referee)
Creation of optimal cutting plans is an important task in many types of industry. In this work we present a novel evolutionary algorithm designed to deal with this problem. The algorithm assumes rectangular shapes of the objects and creates a cutting plan which is can be cut out using a circular saw. The output is presented in a form usable by automatic saws as well as graphically. The algorithm reduces the amount of the material used and, moreover, also reduces the number of needed employees.
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Graph Clustering by Means of Evolutionary Algorithms
Kohout, Jan ; Neruda, Roman (advisor) ; Mrázová, Iveta (referee)
Partitioning nodes of a graph into clusters according to their simi- larities can be a very useful but complex task of data analysis. Many dierent approaches and algorithms for this problem exist, one of the possibilities is to utilize genetic algorithms for solving this type of task. In this work, we analyze dierent approaches to clustering in general and in the domain of graphs. Several clustering algorithms based on the concept of genetic algorithm are proposed and experimentally evaluated. A server application that contains implementations of the these algorithms was developed and is attached to this thesis.
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Moderní evoluční algoritmy pro hledání oblastí s vysokou fitness
Káldy, Martin ; Holeňa, Martin (advisor) ; Gemrot, Jakub (referee)
Evolutionary algorithms are optimization techniques inspired by the actual evolution of biological species. They use conceptually simple process of two repeating phases of reproduction and fitness-based selection, that iteratively evolves each time better solutions. Evolutionary algorithms receive a lot of attention for being able to solve very hard optimization problems, where other optimization techniques might fail due to existence of many local optima. Wide range of different variants of evolutionary algorithms have been proposed. In this thesis, we will focus on the area of Estimation of Distribution Algorithms (EDA). When creating the next generation, EDAs transform the selected high-fitness population into a probability distribution. New generation is obtained by sampling the estimated distribution. We will design and and implement combinations of existing EDAs that will operate in business-specific environment, that can be characterized as tree-like structure of both discrete and continuous variables. Also, additional linear inequality constraints are specified to applicable solutions. Implemented application communicates with provided interfaces, retrieving the problem model specification and storing populations into database. Database is used to assign externally computed fitness values from...
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Cartesian Genetic Programming in Evolutionary Art
Veselý, Pavel ; Hyrš, Martin (referee) ; Petrlík, Jiří (advisor)
This thesis deals with use of Cartesian Genetic Programming (CGP) in Evolutionary Art (EvoArt). Text presents introduction to the topic. The rest of the thesis focuses on the process of design, implementation and testing of new method of application CGP in EvoArt. The proposed method uses CGP to create 2D vector images. Web application for EvoArt creation is made to demonstrate this method. Achieved results are presented and evaluated.
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Evolutionary Design Using Rewriting Systems
Nétková, Barbora ; Hyrš, Martin (referee) ; Bidlo, Michal (advisor)
This master’s thesis proposes a method for the evolutionary design of rewriting systems. In particular, genetic algorithm will be applied to design rewriting rules for a specific variant of Lindenmayer system. The evolved rules of such grammar will be applied to generate growing sorting networks. Some distinct approaches to the rewriting process and construction of the sorting networks will be investigated. It will be shown that the evolution is able to successfully design rewriting rules for the proposed variants of rewriting processes. The results obtained exhibit abilities to successfully create partially growing sorting networks, which was evolved to grow for fewer inputs and in subsequent iterations grows up to 36 inputs.
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