873 resultados para Genetic Programming, NPR, Evolutionary Art


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This work explores the creation of ambiguous images, i.e., images that may induce multistable perception, by evolutionary means. Ambiguous images are created using a general purpose approach, composed of an expression-based evolutionary engine and a set of object detectors, which are trained in advance using Machine Learning techniques. Images are evolved using Genetic Programming and object detectors are used to classify them. The information gathered during classification is used to assign fitness. In a first stage, the system is used to evolve images that resemble a single object. In a second stage, the discovery of ambiguous images is promoted by combining pairs of object detectors. The analysis of the results highlights the ability of the system to evolve ambiguous images and the differences between computational and human ambiguous images.

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The aim of this work is distributed genetic algorithm implementation (so called island algorithm) to accelerate the optimum searching process in space of solutions. Distributed genetic algorithm has also smaller chances to fall in local optimum. This conception depends on mutual cooperation of the clients which realize separate working of genetic algorithms on local machines. As a tool for implementation of distributed genetic algorithm, created to produce net's applications Java technology was chosen. In Java technology, there is a technique of remote methods invocation - Java RMI. By means of invoking remote methods it can send objects between clients and server RMI.

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This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.

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As order dependencies between process tasks can get complex, it is easy to make mistakes in process model design, especially behavioral ones such as deadlocks. Notions such as soundness formalize behavioral errors and tools exist that can identify such errors. However these tools do not provide assistance with the correction of the process models. Error correction can be very challenging as the intentions of the process modeler are not known and there may be many ways in which an error can be corrected. We present a novel technique for automatic error correction in process models based on simulated annealing. Via this technique a number of process model alternatives are identified that resolve one or more errors in the original model. The technique is implemented and validated on a sample of industrial process models. The tests show that at least one sound solution can be found for each input model and that the response times are short.

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Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

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We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.

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Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

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In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

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Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.

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Esta dissertação apresenta um sistema de indução de classificadores fuzzy. Ao invés de utilizar a abordagem tradicional de sistemas fuzzy baseados em regras, foi utilizado o modelo de Árvore de Padrões Fuzzy(APF), que é um modelo hierárquico, com uma estrutura baseada em árvores que possuem como nós internos operadores lógicos fuzzy e as folhas são compostas pela associação de termos fuzzy com os atributos de entrada. O classificador foi obtido sintetizando uma árvore para cada classe, esta árvore será uma descrição lógica da classe o que permite analisar e interpretar como é feita a classificação. O método de aprendizado originalmente concebido para a APF foi substituído pela Programação Genética Cartesiana com o intuito de explorar melhor o espaço de busca. O classificador APF foi comparado com as Máquinas de Vetores de Suporte, K-Vizinhos mais próximos, florestas aleatórias e outros métodos Fuzzy-Genéticos em diversas bases de dados do UCI Machine Learning Repository e observou-se que o classificador APF apresenta resultados competitivos. Ele também foi comparado com o método de aprendizado original e obteve resultados comparáveis com árvores mais compactas e com um menor número de avaliações.

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银杉(Cathaya argyrophylla)是中国特有的濒危裸子植物,孤立分布于我国亚热带山地。虽然以往等位酶和RAPD标记的研究表明,银杉群体的遗传多样性水平很低而群体间的遗传分化很高,但迄今对该种群体的动态变化以及物种的进化历史仍缺乏了解,包括影响群体遗传结构的因素以及物种可能的避难所等,对如何有效地保护和恢复银杉群体仍缺乏科学依据。本文根据8个核基因片段和2个线粒体片段的序列数据,运用群体遗传学和谱系地理学方法,探讨了银杉在DNA水平上的多样性和群体的动态历史,探讨了影响银杉特殊的群体遗传结构的各种因素,并推测了银杉第四纪冰期的避难所,对银杉花粉活力及其变异进行检测。在此基础上,提出了保护和恢复银杉群体的具体策略和措施。主要研究结果如下:   1. 核甘酸多态性和群体遗传结构   从101个核基因片段中筛选了8个适用于银杉的片段,对来自四个地区15个群体共86个个体的胚乳总DNA进行了扩增和测序。8个核基因座位的平均核苷酸多态性(θs=0.0022,πs=0.0027)显著低于其它松杉植物遗传多态性的多座位估计值。四个地区中,大瑶山(DY)的多态性最高(θs=0.0026,πs=0.0027),八面山(BM)最低(θs=0.0014,πs=0.0018),大娄山(DL)和越城岭(YC)介于二者之间。大多数座位内的多态位点间紧密连锁,座位间的连锁只在八面山地区检测到。AMOVA分析表明显著性的多态性比例存在于地区间(20.05%)和地区内群体间(9.37%)。FST分析也显示群体间(FST=0.294)和地区间(FST =0.131-0.319)存在显著的遗传分化。推测伴随着瓶颈效应而出现的遗传漂变及其有限的基因流(Nm=1.2)是导致银杉群体低水平多态性和群体间强烈分化的主要原因。   2.谱系地理学分析   利用2个线粒体片段(nad1和nad4)序列以及高变异量的核2009片段序列对银杉的谱系地理进行了探讨。2个线粒体片段的多态位点组合成3种单倍型,将银杉分成大娄山(DL)、八面山(BM)以及越城岭(YC)和大瑶山(DY)3个地区,地区间的单倍型完全不同(GST=1.0),结合核基因呈现的群体遗传结构,推测现存银杉群体至少来源于4个冰期避难所,相当于银杉现存的4个相互隔离的地区。2009座位上12个变异位点组合成8种单倍型,位于单倍型谱系内部节点的4种祖先单倍型分布广泛、出现频率最高,其它7个核基因座位具有类似谱系结构。遗传距离和地理距离没有相关性,NST (0.138)与GST (0.134)没有显著性差异,说明现存的银杉群体是相对较近的时间内片断化的产物。2009片段分离位点的失配分布(mismatch distribution)呈双峰和多峰,表明银杉群体没有经历近期的扩张,与古生物学研究证据相吻合。   3. 银杉的基因杂合性和花粉生命力   利用2009和cad两个核基因片段,采用多胚乳序列法得到总的杂合体比率为64%(2009)和60%(cad),说明银杉群体中存在高比例的杂合体。大娄山地区的杂合体比率是八面山地区的2倍。银杉杂合体比率的高低可能与其遗传多态性有关,也可能是自然选择的结果。采用TTC染色法对银杉的花粉生命力进行了测定,在干燥低温条件下银杉花粉的活力很稳定,保存一年后有活力的花粉数仍高达80%以上。通过对来自两个地区(DY和YC)7个群体共16个银杉个体花粉活力的测定发现,银杉有活力花粉比例高达93.3%,与其它裸子植物相当。花粉活力在地区间和群体间存在显著差异,花坪地区(95.2%)的花粉活力高于大瑶山(91.3%)。花粉活力在群体内个体间差异不显著。   4.银杉的进化历史及其保护   银杉的低水平的遗传多样性和独特的群体遗传结构对我们推测其冰川期避难所提供了重要依据。本研究在银杉4个孤立分布区发现了彼此不重叠的线粒体单倍型,同时核基因表现出了4个地理群,说明随着第四纪冰期气候的波动和银杉分布范围的片段化,原来广布的群体逐步萎缩,最后被保留在位于西部大娄山(DL),东部八面山(BM),中南部越城岭(YC)和南部大瑶山(DY) 4个相互隔离的避难所。银杉独特的群体结构和动态历史对进一步制定相应的保护措施具有重要参考价值。由于遗传多态性很低,群体又小,几乎所有现存的银杉群体都面临由于随机事件导致的物种灭绝。更严重的是,当前该物种的适生环境不断恶化和片段化,以及异常低的生殖和存活率导致银杉与其它物种竞争能力很低。因此,除目前采取的原地保护策略外,迁地保护应给予优先考虑。此外,采用传统的控制授粉策略(在遗传上有显著差异的群体间开展人工杂交)是丰富其遗传多态性、恢复衰退群体的可行措施之一。

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将密码协议与密码算法视为一个系统,建立了密码协议系统的一种安全模型.基于假设/保证的组合推理技术提出了新的假设/保证推理规则和假设/保证推理算法,证明了该规则的完备性,实现了密码协议系统的模型检查,并重点解决了系统分解问题、假设函数的设定问题、进程+逻辑的系统特性描述问题等难题.以kerberos密码协议系统为例,利用该安全模型和假设/保证推理技术对密码协议系统进行了安全验证.

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异常检测技术假设所有的入侵行为都会偏离正常行为模式.尝试寻找一种新的异常入侵检测模型改善准确性和效率.模型利用应用程序的系统调用序列,通过基因规划建立了正常行为模式.模型的一个例程管理一个进程.当它发现进程的实际系统调用序列模式偏离正常的行为模式时,会将进程设标记为入侵,并采取应急措施.还给出了基因规划的适应度计算方法以及两个生成下一代的基本算子.通过与现有一些模型的比较,该模型具有更好的准确性和更高的效率.

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In this paper, I describe the application of genetic programming to evolve a controller for a robotic tank in a simulated environment. The purpose is to explore how genetic techniques can best be applied to produce controllers based on subsumption and behavior oriented languages such as REX. As part of my implementation, I developed TableRex, a modification of REX that can be expressed on a fixed-length genome. Using a fixed subsumption architecture of TableRex modules, I evolved robots that beat some of the most competitive hand-coded adversaries.