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Evolutionary Analogue Amplifier Optimisation
Bielik, Marek ; Zachariášová, Marcela (referee) ; Bidlo, Michal (advisor)
Táto práca demonštruje možnosti využitia evolučných algoritmov, konkrétne evolučných stratégií, v doméne dizajnu analógových zosilňovačov. Do implementácie je zahrnutý ngSPICE simulátor, ktorý je použitý na vyhodnotenie optimalizovaných riešení a v práci je navrhnutých niekoľko vyhodnocovacích metód. Práca tiež zahŕňa experimenty a ich výsledky, ktoré boli použité na určenie najvodnejších parametrov evolučných stratégií. Cieľom bolo optimalizovať hodnoty súčiastok jedno a dvoj stupňových zosilňovačov s bipolárnymi tranzistormi v zapojení so spoločným emitorom. Výsledkom je nástroj umožňujúci návrh zosilňovačov s ľubovoľným zosilnením v rámci možností daného obvodu bez použitia akéhokoľvek matematického aparátu.
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Learnable Evolution Model for Optimization (LEM)
Weiss, Martin ; Vašíček, Zdeněk (referee) ; Schwarz, Josef (advisor)
Numerical optimization of multimodal or otherwise nontrivial functions has stayed around the peak of the interest of many researchers for a long time. One of the promising methods that appeared is the hybrid approach of the Learnable Evolution Model that combines the well-established ways of artificial intelligence and machine learning with recently popular and efective methods of evolutionary programming. In this work, the method itself was reviewed with respect to what has been already implemented and tested and several possible new implementations of the method were proposed and some of them consequently implemented. The resulting program was then tested against a set of chosen nontrivial real-valued functions and its results were compared to those achieved with EDA algorithms.
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Optimization of PID controller using evolutionary computing techniques
Kočí, Jakub ; Matoušek, Radomil (referee) ; Lang, Stanislav (advisor)
This bachelor thesis deals with using evolutionary computation for tuning up PID controller. In research part there are summarised information about regulation and another background information about quality of regulation and ITAE criterion. Practical part consist of implementing three evolutionary computation algorithms - differential evolution, evolution strategy and genetic algorithm. These and MATLAB's function ga() are compared on two systems mutually and to Ziegler-Nichols rule. Basic comparsion is followed by statistical evaluation on second system.
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Brain Storm Optimization algorithm: variants and applications
Řezáč, Ladislav ; Hůlka, Tomáš (referee) ; Kůdela, Jakub (advisor)
The thesis contains an introduction to topic optimization. It is devoted to the description of swarming and evolutionary algorithms and to a minor rearrangement of selected models. Finally, it moves to the better state of this survey of the moment and the history of one of the new models of the group, more precisely the Brain storm optimization algorithm. It was subsequently tested for the sensitivity of individual parameters and the accuracy of the results of the solutions found across several test functions. Finally, the best variants of BSO were compared with other algorithms according to calculation times and achieved results.
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Optimization of PID controller using evolutionary computing techniques
Kočí, Jakub ; Matoušek, Radomil (referee) ; Lang, Stanislav (advisor)
This bachelor thesis deals with using evolutionary computation for tuning up PID controller. In research part there are summarised information about regulation and another background information about quality of regulation and ITAE criterion. Practical part consist of implementing three evolutionary computation algorithms - differential evolution, evolution strategy and genetic algorithm. These and MATLAB's function ga() are compared on two systems mutually and to Ziegler-Nichols rule. Basic comparsion is followed by statistical evaluation on second system.
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Evolutionary Analogue Amplifier Optimisation
Bielik, Marek ; Zachariášová, Marcela (referee) ; Bidlo, Michal (advisor)
Táto práca demonštruje možnosti využitia evolučných algoritmov, konkrétne evolučných stratégií, v doméne dizajnu analógových zosilňovačov. Do implementácie je zahrnutý ngSPICE simulátor, ktorý je použitý na vyhodnotenie optimalizovaných riešení a v práci je navrhnutých niekoľko vyhodnocovacích metód. Práca tiež zahŕňa experimenty a ich výsledky, ktoré boli použité na určenie najvodnejších parametrov evolučných stratégií. Cieľom bolo optimalizovať hodnoty súčiastok jedno a dvoj stupňových zosilňovačov s bipolárnymi tranzistormi v zapojení so spoločným emitorom. Výsledkom je nástroj umožňujúci návrh zosilňovačov s ľubovoľným zosilnením v rámci možností daného obvodu bez použitia akéhokoľvek matematického aparátu.
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Evolutionary techniques utilization in hierarchical task network
Řeháková, Lucie ; Neruda, Roman (advisor) ; Pilát, Martin (referee)
This master thesis describes the design and the implementation of the algorithm solving the domain- independent partial order simple task network planning problem using the tree-based genetic programming. The work contains comparison of several possible approaches to the problem --- it compares different representations, ways of evaluation and approaches to the partial ordering. It defines heuristics to improve the efficiency of the algorithm, including the distance heuristic, the local search and the individual equivalency. The implementation was tested on several experiments to show the abilities, strengths and weaknesses of the algorithm. Powered by TCPDF (www.tcpdf.org)
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Inspiration-triggered search: Towards higher complexities by mimicking creative processes
Rybář, Milan ; Hamann, Heiko (advisor) ; Majerech, Vladan (referee)
The trap of local optima is one of the main challenges of stochastic optimization methods from machine learning. The aim of this thesis is to develop an optimization algorithm that is inspired by users interacting with Picbreeder, which is an online service that allows users to collaboratively evolve images via an artificial evolution. The idea is that their behaviours depict creative processes. We propose a general framework on the top of a common optimization technique called inspiration-triggered search, which mimics these processes. Instead of a fixed objective function the search algorithm is free to change the objective within certain constraints. The overall optimization task is to generate complex artefacts that cannot be generated by a greedy and direct optimization approach. The proposed method is tested in the domain of images, that is to find complex and aesthetically pleasant images for humans, and compared with the direct optimization. Powered by TCPDF (www.tcpdf.org)
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Learnable Evolution Model for Optimization (LEM)
Weiss, Martin ; Vašíček, Zdeněk (referee) ; Schwarz, Josef (advisor)
Numerical optimization of multimodal or otherwise nontrivial functions has stayed around the peak of the interest of many researchers for a long time. One of the promising methods that appeared is the hybrid approach of the Learnable Evolution Model that combines the well-established ways of artificial intelligence and machine learning with recently popular and efective methods of evolutionary programming. In this work, the method itself was reviewed with respect to what has been already implemented and tested and several possible new implementations of the method were proposed and some of them consequently implemented. The resulting program was then tested against a set of chosen nontrivial real-valued functions and its results were compared to those achieved with EDA algorithms.
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Special Issue on Hybrid Intelligent Systems 2007
Abraham, A. ; Húsek, Dušan ; Snášel, V.
Special Issue on Hybrid Intelligent Systems 2007. Neural Network World. Vol. 17, No. 6 (2007), p.505-688 The issue contains papers prepared specially for this issue by authors of some best evaluated papers presented on HIS'07) at Kaiserslautern, Germany, during September 17-19, 2007. The Current research interests in HIS and covered in this issue focus on integration of the different computing paradigms such as fuzzy logic, euro-computation, evolutionary computation, probabilistic computing, intelligent agents, machine learning, and other intelligent computing frameworks. There is also a growing interest in the role of sensors, their integration and evaluation in such frameworks. The phenomenal growth of hybrid intelligent systems and related topics has obliged.
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