|
|
Coevolution in Evolutionary Circuit Design
Veřmiřovský, Jakub ; Hrbáček, Radek (referee) ; Drahošová, Michaela (advisor)
This thesis deals with evolutionary design of the digital circuits performed by a cartesian genetic programing and optimization by a coevolution. Algorithm coevolves fitness predictors that are optimized for a population of candidate digital circuits. The thesis presents theoretical basis, especially genetic programming, coevolution in genetic programming, design of the digital circuits, and deals with possibilities of the utilization of the coevolution in the combinational circuit design. On the basis of this proposal, the application designing and optimizing logical circuits is implemented. Application functionality is verified in the five test tasks. The comparison between Cartesian genetic programming with and without coevolution is considered. Then logical circuits evolved using cartesian genetic programming with and without coevolution is compared with conventional design methods. Evolution using coevolution has reduced the number of evaluation of circuits during evolution in comparison with standard cartesian genetic programming without coevolution and in some cases is found solution with better parameters (i.e. less logical gates or less delay).
|
|
|
Symbolic Regression and Coevolution
Drahošová, Michaela ; Žaloudek, Luděk (referee) ; Sekanina, Lukáš (advisor)
Symbolic regression is the problem of identifying the mathematic description of a hidden system from experimental data. Symbolic regression is closely related to general machine learning. This work deals with symbolic regression and its solution based on the principle of genetic programming and coevolution. Genetic programming is the evolution based machine learning method, which automaticaly generates whole programs in the given programming language. Coevolution of fitness predictors is the optimalization method of the fitness modelling that reduces the fitness evaluation cost and frequency, while maintainig evolutionary progress. This work deals with concept and implementation of the solution of symbolic regression using coevolution of fitness predictors, and its comparison to a solution without coevolution. Experiments were performed using cartesian genetic programming.
|
|
|
Coevolutionary Algorithm in FPGA
Hrbáček, Radek ; Vašíček, Zdeněk (referee) ; Drahošová, Michaela (advisor)
This thesis deals with the design of a hardware acceleration unit for digital image filter design using coevolutionary algorithms. The first part introduces reconfigurable logic device technology that the acceleration unit is based on. The theoretical part also briefly characterizes evolutionary and coevolutionary algorithms, their principles and applications. Traditional image filter designs are compared with the biologically inspired design methods. The hardware unit presented in this thesis exploits dual MicroBlaze system extended by custom peripherals to accelerate cartesian genetic programming. The coevolutionary image filter design is accelerated up to 58 times. The hardware platform functionality in the task of impulse noise filter design and edge detector design has been empirically analyzed.
|
|
|
Coevolution of Cartesian Genetic Algorithms and Neural Networks
Kolář, Adam ; Král, Jiří (referee) ; Zbořil, František (advisor)
The aim of the thesis is to verify synergy of genetic programming and neural networks. Solution is provided by set of experiments with implemented library built upon benchmark tasks. I've done experiments with directly and also indirectly encoded neural netwrok. I focused on finding robust solutions and the best calculation of configurations, overfitting detection and advanced stimulations of solution with fitness function. Generally better solutions were found using lower values of parameters n_c and n_r. These solutions tended less to be overfitted. I was able to evolve neurocontroller eliminating oscilations in pole balancing problem. In cancer detection problem, precision of provided solution was over 98%, which overcame compared techniques. I succeeded also in designing of maze model, where agent was able to perform multistep tasks.
|
|
|
Insect herbivores drive the loss of unique chemical defense in willows
VOLF, Martin
The thesis examines the effects of chemical and mechanical defensive traits on insects in a local community of 11 Salicaceae species growing in sympatry. The results repeated loss of willow specialized chemical defense. This could be due to its low protective value and high energy costs. Our study thus shows that the balance between costs and benefits of defensive traits is not necessarily in favor of specialized defenses and illustrates a process, which may lead to the reduction in a defensive trait.
|
|
|
The symbiotic bacteria of lice of genus \kur{Polyplax}: general phylogenetic and genomic characterisation
ŘÍHOVÁ, Jana
Blood-sucking lice of the genus Polyplax harbour two bacterial endosymbionts, one from family Legionellales and the other from family Neisseriales. Fylogenetic and genomic analyses reveal typical features of endosymbiotic bacteria and coevolution history with the host. In the genome of the endosymbiont from family Legionellales I found operon, which encodes biosynthesis of biotin, essential vitamin, especially for blood-sucking insects living on low-nutrient diet. This operon was probably horizontally transfer from Wolbachia, nutrient endosymbiont of bedbug Cimex lectularius.
|
|
| |
guest ::
