Tato diplomová práce se zabývá  aproximativní implementace aritmetických operací v obrazových filtrech. Zejména tedy využitím aproximativních technik pro úpravu způsobu násobení v netriviálním obrazovém filtru. K tomu je využito několik technik, jako použití převodu násobení s pohyblivou řadovou čárkou na násobení s pevnou řadovou čárkou, či využití evolučních algoritmů zejména kartézkého genetického programování pro vytvoření nových aproximovaných násobiček, které vykazují přijatelnou chybu, ale současně redukují výpočetní náročnost filtrace. Výsledkem jsou evolučně navržené aproximativní násobičky zohledňující distribuci dat v obrazovém filtru a jejich nasazení v obrazovém filtru a porovnání původního filtru s aproximovaným fitrem na sadě barevných obrázků.

Cartesian genetic programming (CGP) is a form of genetic programming where candidate programs are represented in the form of directed acyclic graphs. It was shown that CGP can be accelerated using coevolution with a population of fitness predictors which are used to estimate the quality of candidate solutions. The major disadvantage of the coevolutionary approach is the necessity of performing many time-consuming experiments to determine the best size of the fitness predictor for the particular task. This project introduces a new fitness predictor representation with phenotype plasticity, based on the principles of colearning in evolutionary algorithms. Phenotype plasticity allows to derive various phenotypes from the same genotype. This allows to adapt the size of the predictors to the current state of the evolution and difficulty of the solved problem. The proposed algorithm was implemented in the C language and optimized using SSE2 and AVX2 vector instructions. The experimental results show that the resulting image filters are comparable with standard CGP in terms of filtering quality. The average speedup is 8.6 compared to standard CGP. The speed is comparable to standard coevolutionary CGP but it is not necessary to experimentally determine the best size of the fitness predictor while applying coevolution to a new, unknown task.

This work aims to design process of integrated circuits on the transistor level, specially using evolutionary algorithm. For this purpose it is necessary to choose reasonable level of abstraction during simulation, which is used for evaluation candidate solutions by fitness function. This simulation has to be fast enough to evaluate thousands of candidate solutions within seconds. This work discusses already used techniques for transistor level circuit design and it chooses useful parts for new design of faster and more reliable automated design process, which would be able to design complex logic circuits. The thesis also discusses implementation of this system and used approach with regard to encountered problems in transistor-level circuit design and optimization by evolution.

This work deals with part of the encryption algorithm, called S-box and its development. For its development is used evolutionary computing, such as classical genetic algorithm, Estimation of Distribution Algorithm, Cartesian genetic programming and multi-criteria VEGA and SPEA algorithms. This thesis aims to test the properties of substitution boxes to its evolutionary development. Firstly, the work deals with cryptography and issues of s-boxes. There are explained basic concepts and describes the selected criteria of safety. Next chapter explains evolutionary algorithms   and multi-criteria optimization. This knowledge is used to design and program implementation, which are described below. Finally discusses the application of the criteria studied. Discussed here is searching S-boxes in both single-criteria, and especially in multi-criteria genetic search.

This thesis summarizes knowledge in the field of time series theory, method for time series analysis and applications in financial modeling. It also resumes the area of evolutionary algorithms, their classification and applications. The core of this work combines these knowledges in order to build a system utilizing evolutionary algorithms for financial time series forecasting models optimization. Various software engineering techniques were used during the implementation phase (ACI - autonomous continual integration, autonomous quality control etc.) to ensure easy maintainability and extendibility of project by more developers.

In the nearest future, the human driver is viewed as a reliable backup even for the fully automated road motor vehicles (cars). Indeed, the driver is assumed to swiftly take the control of the car in cases of suddenly occurring (i) challenging environmental conditions, (ii) complex unforeseen driving situations, or (iii) degradation of performance of the car. However, due to the cognitive overload in such a sudden, stressful takeover of the control, the driver would often experience the startle effect, which usually results in an unconscious, instinctive, yet incorrect response. An extreme case of startle is freezing, in which the driver might be incapable to respond to the sudden takeover of control at all. The possible approaches to alleviate the startle during the takeover of control (i.e., the automation startle) include an offset- (i.e., either early- or delayed-), gradual yielding the controls to the driver. In the cases considered above, however, these approaches are hardly applicable because of (i) the presumed unpredictability of the events that result in the need of takeover of control, and (ii) the severe time constraints of the latter. Conversely, the objective of our research is to propose an approach of minimizing the need of yielding the control to the driver in challenging environmental conditions by guaranteeing an adequate automated control in these conditions. Focusing on slippery roads as an instance of challenging conditions, and steering control as an instance of control, we aim at developing such an automated steering that controls the car adequately in various road surfaces featuring low friction coefficients without the need of driver’s intervention.In order to develop such an automated steering we employed an in-house evolutionary computation framework – XML-based genetic programming (XGP) – which offers a flexible, portable, and human readable representation of the evolved optimal steering functions. The trial runs of the evolved steering functions were performed in the Open Source Racing Car Simulator (TORCS), which features a realistic, yet computationally efficient simulation of the car and its environment. The obtained experimental results indicate that due to the challenging dynamics of the unstable car on slippery roads, neither the canonical (tuned) servo-control (as a variant of PD) nor the (tuned) PID-controller could control the car adequately on slippery roads. On the other hand, the controller, featuring a relaxed, arbitrary structure evolved by XGP outperforms both the servo- and PID controllers in that it results in a minimal deviation of the car from its intended trajectory in rainy, snowy, and icy road conditions. Moreover, the evolved steering that employs anticipated perceptions is even superior as it could anticipate the imminent understeering of the car at the entry of the turns and consequently – to compensate for such an understeering by proactively turning the steering wheels in advance – well before entering the turn. The obtained results suggest a human competitiveness of the evolved automated steering as it outperforms the commonly used alternative steering controllers proposed by human experts. The research could be viewed as a step towards the evolutionary development of automated steering of cars in challenging environmental conditions. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

En la actualidad, el uso de las tecnologías ha sido primordial para el avance de las sociedades, estas han permitido que personas sin conocimientos informáticos o usuarios llamados “no expertos” se interesen en su uso, razón por la cual los investigadores científicos se han visto en la necesidad de producir estudios que permitan la adaptación de sistemas, a la problemática existente dentro del ámbito informático. Una necesidad recurrente de todo usuario de un sistema es la gestión de la información, la cual se puede administrar por medio de una base de datos y lenguaje específico, como lo es el SQL (Structured Query Language), pero esto obliga al usuario sin conocimientos a acudir a un especialista para su diseño y construcción, lo cual se ve reflejado en costos y métodos complejos, entonces se plantea una pregunta ¿qué hacer cuando los proyectos son pequeñas y los recursos y procesos son limitados? Teniendo como base la investigación realizada por la universidad de Washington[39], donde sintetizan sentencias SQL a partir de ejemplos de entrada y salida, se pretende con esta memoria automatizar el proceso y aplicar una técnica diferente de aprendizaje, para lo cual utiliza una aproximación evolucionista, donde la aplicación de un algoritmo genético adaptado origina sentencias SQL válidas que responden a las condiciones establecidas por los ejemplos de entrada y salida dados por el usuario. Se obtuvo como resultado de la aproximación, una herramienta denominada EvoSQL que fue validada en este estudio. Sobre los 28 ejercicios empleados por la investigación [39], 23 de los cuales se obtuvieron resultados perfectos y 5 ejercicios sin éxito, esto representa un 82.1% de efectividad. Esta efectividad es superior en un 10.7% al establecido por la herramienta desarrollada en [39] SQLSynthesizer y 75% más alto que la herramienta siguiente más próxima Query by Output QBO[31]. El promedio obtenido en la ejecución de cada ejercicio fue de 3 minutos y 11 segundos, este tiempo es superior al establecido por SQLSynthesizer; sin embargo, en la medida un algoritmo genético supone la existencia de fases que amplían los rangos de tiempos, por lo cual el tiempo obtenido es aceptable con relación a las aplicaciones de este tipo. En conclusión y según lo anteriormente expuesto, se obtuvo una herramienta automática con una aproximación evolucionista, con buenos resultados y un proceso simple para el usuario “no experto”. / Actuellement l'usage des technologies est primordial pour l'avance de la société, celles-ci ont permis que des personnes sans connaissances informatiques ou des utilisateurs appelés "non expert" s'intéressent à son usage. C'est la raison pour laquelle les enquêteurs scientifiques se sont vus dans la nécessité de produire les études qui permettent l'adaptation des systèmes à la problématique existante à l'intérieur du domaine informatique. Une nécessité récurrente pour tout utilisateur d'un système est la gestion de l'information, que l’on peut administrer au moyen d'une base de données et de langage spécifique pour celles-ci comme est le SQL (Structured Query Language), mais qui oblige à l'utilisateur à chercher un spécialiste pour sa conception et sa construction, et qui représente des prix et des méthodes complexes. Une question se pose alors, quoi faire quand les projets sont petites et les ressources et les processus limités ? Ayant pour base la recherche de l'université de Washington [39], ce mémoire automatise le processus et applique une différente technique d'apprentissage qui utilise une approche évolutionniste, où l'application d'un algorithme génétique adapté génère des requêtes SQL valides répondant aux conditions établies par les exemples d'entrée et de sortie donnés par l'utilisateur. On a obtenu comme résultat de l’approche un outil dénommé EvoSQL qui a été validé dans cette étude. Sur les 28 exercices employés par la recherche [39], 23 exercices ont été obtenus avec des résultats parfaits et 5 exercices sans succès, ce qui représente 82.1 % d'effectivité. Cette effectivité est supérieure de 10.7 % à celle établie par l'outil développé dans [32] SQLSynthesizer et 75% plus haute que l'outil suivant le plus proche Query by Output QBO [31]. La moyenne obtenue dans l'exécution de chaque exercice a été de 3 min et 11sec, ce qui est supérieur au temps établi par SQlSynthesizer, cependant dans la mesure où un algorithme génétique suppose que l'existence de phases augmente les rangs des temps, le temps obtenu est acceptable par rapport aux applications de ce type. Dans une conclusion et selon ce qui a été antérieurement exposé nous avons obtenu un outil automatique, avec une approche évolutionniste, avec de bons résultats et un processus simple pour l'utilisateur « non expert ». / At present the use of the technologies is basic for the advance of the society; these have allowed that persons without knowledge or so called "non expert" users are interested in this use, is for it that the researchers have seen the need to produce studies that allow the adjustment of the systems the existing at the problematic inside the area of the technology. A need of every user of a system is the management of the information, which can be manage by a database and specific language for these as the SQL (Structured Query Language), which forces the user to come to a specialist for the design and construction of this one, which represents costs and complex methods, but what to do when they are small investigations where the resources and processes are limited? Taking as a base the research of the university of Washington [32], this report automates the process and applies a different learning technique, for which uses an evolutionary approach, where the application of a genetic adapted algorithm generates query SQL valid that answer to the conditions established by the given examples of entry and exit given by the user. There was obtained as a result of the approach a tool named EvoSQL that was validated in the same 28 exercises used by the investigation [32], of which 23 exercises were obtained by ideal results and 5 not successful exercises, which represents 82.1 % of efficiency, superior in 10.7 % to the established one for the tool developed in [32] SQLSynthesizer and 75% higher than the following near tool Query by Output QBO [26]. The average obtained in the execution of every exercise was of 3 min and 11seg that is superior to the time established by SQlSynthesizer, Nevertheless, being a genetic algorithm where the steps existence makes that the ranges of times are extended, the obtained one is acceptable with relation to the applications of this type. In conclusion et according to previously exposed, we have obtained an automatic tool, with an evolutionary approach, with good results and a simple process for the « not expert » user.

為了瞭解影響演化性神經網路（ENT）預測表現的四項重要的機制：輸入資料性質、訓練樣本大小、網路搜尋密度以及控制模型複雜度，進而找出能使ENT充分發揮效果的組合。在本論文中首先設計ENT在模擬資料上的實驗，探討上述四項機制個別對預測表現的影響，再依照實驗結果的建議，設計能讓ENT發揮功效的組合，並以實際金融高頻率資料：香港恆生指數在一九九八年十二月報酬率為標的，探討模擬資料的結果在實際金融資料需要調整的部份。實驗結果顯示，當輸入資料經過線性過濾後，搭配大樣本訓練、高搜尋強度與適當地模型複雜度控制，會是能讓神經網路提高預測能力的組合。在實際金融資料的實驗當中同時發現，資料中偶而出現特別高或特別低的變化，會對ENT的預測表現有相當程度的影響。 / In this thesis, Evolutionary Neural Trees (ENTs) are applied to forecast the artificial data generated by financial and chaos models — iid random, linear process (Auto Regressive-Moving Average；ARMA), nonlinear processes (AutoRegressive Conditional Heteroskedasticity；ARCH, General AutoRegressive Conditional Heteroskedasticity；GARCH, Bilinear), mixed linear and nonlinear process (AR and GARCH). Experiments of the artificial data were conducted to understand the characteristics of ENTs mechanism. – data pre-processing procedures, search intensity, sample size and complexity regularization. From the experiment results of artificial data, the combination of pure linear or nonlinear time series, large sample size, intensive search and simple neural trees are suggested for the parameters setting of ENTs. And for the sake of computational burden, we have a trade-off between search intensity and sample size. Ten experiments are designed for ENTs modeling on the high-frequency stock returns of Heng Sheng stock index on December, 1998, in order to have an efficient combination of the factors of ENTs. The results show that ENTs would perform more efficiently if data are pre-processed by a linear filter, for ENTs will concentrate on searching in the space of nonlinear signals. Also, as is well demonstrated in this study, the infrequent bursts (outliers) appearing in the data set can be very disturbing for the ENTs modeling.

我們把經濟體視為一個複雜適應系統(complex adaptive system), 強調系統中異質性(heterogeneous)agent的學習適應行為與agent之間的互動性交互作用, 此時主流經濟學裡的分析架構, 如:代表性個人模型(represesentive agent model)、理性預期(rational expectation)、固定點均衡分析(fixed-point equilibrium analysis)等將不再適用, 取而代之的是演化經濟學(evolutionary economics)的研究典範, 這樣的研究架構下, 並沒有適當的數學分析工具可資運用, 因此我們改以agent-based建模(agent-based modelng)的社會模擬(social simulation)來建構一個人工的經濟體(artificial economy), 以此為主要研究方法, 這就是agent-based計算經濟學(agent-based computational economics)或稱人工經濟生命(artificial economic life)。 本文中以股票市場為主要的研究課題, 我們以遺傳規劃(genetic programming)的人工智慧(artificial intelligence)方法來模擬股市中有限理性(bounded rational)異質交易者的交易策略學習行為, 建構出一個人工股票市場(artificial stock market), 在這樣的架構下, 我們成功地產生出類似真實股票市場的股價時間序列特性, 我們同時也檢定了人工股票市場中價量的因果關係, 說明了在沒有外生因素之下, 人工股票市場的複雜系統可自發地產生出雙向的價量因果關係, 進一步地, 我們研究下層agent(交易者)行為與上層股價時間序列行為的關聯性, 我們也發現個體的行為並不能直接加總或推論出複雜適應系統的總體行為, 這就是突現性質(emergent property)的發生, 最後, 本文描述了agent-based計算經濟學研究架構的優勢與缺點, 再附帶介紹一個用以進行agent-based建模相關研究的軟體程式庫-SWARM。

知訊者與非知訊者資訊不對稱之議題在學術殿堂一直廣為學者所研究討論，且各國證管機關為了維持證券市場公平性、保護非知訊者權益並且維持股票市場的穩定運作，適度的資訊揭露以維持證券市場的公平性一直都是各國證券交易所重視的政策目標。 是故，本研究利用代理人基人工股票市場來探討資訊揭露對於金融市場之影響。在此架構下之交易者皆已有限理性方式來呈現。他們是以遺傳規劃(genetic programming)之方式來學習並修正他們對於未來之金融市場之預期。在透過即時的模擬價格之資訊揭露，我們嘗試探討此資訊揭露之金融政策措施對於市場之波動性、市場之流動性之影響。 / The topic of asymmetric information between the informed traders and uninformed traders has been widely discussed by researchers in academics. To maintain the fairness of securities market, an appropriate information disclosure is quite important for authorities of securities regulation to protect the rights and interests of uninformed traders, and to maintain the operations of securities market stable. Based on these reasons, we construct an agent-based artificial stock market to investigate how information disclosure affects a financial market. In this framework of artificial stock market, all traders are characterized by bounded rationality. The traders are able to learn and adjust their predictions of financial market by means of a genetic programming algorithm. We try to understand how market transparency affects the volatility and the liquidity of a securities market.