A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Synergistic Visualization And Quantitative Analysis Of Volumetric Medical Images

The medical diagnosis process starts with an interview with the patient, and continues with the physical exam. In practice, the medical professional may require additional screenings to precisely diagnose. Medical imaging is one of the most frequently used non-invasive screening methods to acquire insight of human body. Medical imaging is not only essential for accurate diagnosis, but also it can enable early prevention. Medical data visualization refers to projecting the medical data into a human understandable format at mediums such as 2D or head-mounted displays without causing any interpretation which may lead to clinical intervention. In contrast to the medical visualization, quantification refers to extracting the information in the medical scan to enable the clinicians to make fast and accurate decisions. Despite the extraordinary process both in medical visualization and quantitative radiology, efforts to improve these two complementary fields are often performed independently and synergistic combination is under-studied. Existing image-based software platforms mostly fail to be used in routine clinics due to lack of a unified strategy that guides clinicians both visually and quan- titatively. Hence, there is an urgent need for a bridge connecting the medical visualization and automatic quantification algorithms in the same software platform. In this thesis, we aim to fill this research gap by visualizing medical images interactively from anywhere, and performing a fast, accurate and fully-automatic quantification of the medical imaging data. To end this, we propose several innovative and novel methods. Specifically, we solve the following sub-problems of the ul- timate goal: (1) direct web-based out-of-core volume rendering, (2) robust, accurate, and efficient learning based algorithms to segment highly pathological medical data, (3) automatic landmark- ing for aiding diagnosis and surgical planning and (4) novel artificial intelligence algorithms to determine the sufficient and necessary data to derive large-scale problems

Realistic simulation and animation of clouds using SkewT-LogP diagrams

Nuvens e clima são tópicos importantes em computação gráfica, nomeadamente na simulação e animação de fenómenos naturais. Tal deve-se ao facto de a simulação de fenómenos naturais−onde as nuvens estão incluídas−encontrar aplicações em filmes, jogos e simuladores de voo. Contudo, as técnicas existentes em computação gráfica apenas permitem representações de nuvens simplificadas, tornadas possíveis através de dinâmicas fictícias que imitam a realidade. O problema que este trabalho pretende abordar prende-se com a simulação de nuvens adequadas para utilização em ambientes virtuais, isto é, nuvens com dinâmica baseada em física que variam ao longo do tempo. Em meteorologia é comum usar técnicas de simulação de nuvens baseadas em leis da física, contudoossistemasatmosféricosdeprediçãonuméricasãocomputacionalmente pesados e normalmente possuem maior precisão numérica do que o necessário em computação gráfica. Neste campo, torna-se necessário direcionar e ajustar as características físicas ou contornar a realidade de modo a atingir os objetivos artísticos, sendo um fator fundamental que faz com que a computação gráfica se distinga das ciências físicas. Contudo, simulações puramente baseadas em física geram soluções de acordo com regras predefinidas e tornam-se notoriamente difíceis de controlar. De modo a enfrentar esses desafios desenvolvemos um novo método de simulação de nuvens baseado em física que possui a característica de ser computacionalmente leve e simula as propriedades dinâmicas relacionadas com a formação de nuvens. Este novo modelo evita resolver as equações físicas, ao apresentar uma solução explícita para essas equações através de diagramas termodinâmicos SkewT/LogP. O sistema incorpora dados reais de forma a simular os parâmetros necessários para a formação de nuvens. É especialmente adequado para a simulação de nuvens cumulus que se formam devido ao um processo convectivo. Esta abordagem permite não só reduzir os custos computacionais de métodos baseados em física, mas também fornece a possibilidade de controlar a forma e dinâmica de nuvens através do controlo dos níveis atmosféricos existentes no diagrama SkewT/LogP. Nestatese,abordámostambémumoutrodesafio,queestárelacionadocomasimulação de nuvens orográficas. Do nosso conhecimento, esta é a primeira tentativa de simular a formação deste tipo de nuvens. A novidade deste método reside no fato de este tipo de nuvens serem não convectivas, oque se traduz nocálculodeoutrosníveis atmosféricos. Além disso, atendendo a que este tipo de nuvens se forma sobre montanhas, é também apresentadoumalgoritmoparadeterminarainfluênciadamontanhasobreomovimento da nuvem. Em resumo, esta dissertação apresenta um conjunto de algoritmos para a modelação e simulação de nuvens cumulus e orográficas, recorrendo a diagramas termodinâmicos SkewT/LogP pela primeira vez no campo da computação gráfica.Clouds and weather are important topics in computer graphics, in particular in the simulation and animation of natural phenomena. This is so because simulation of natural phenomena−where clouds are included−find applications in movies, games and flight simulators. However, existing techniques in computer graphics only offer the simplified cloud representations, possibly with fake dynamics that mimic the reality. The problem that this work addresses is how to find realistic simulation of cloud formation and evolution, that are suitable for virtual environments, i.e., clouds with physically-based dynamics over time. It happens that techniques for cloud simulation are available within the area of meteorology, but numerical weather prediction systems based on physics laws are computationally expensive and provide more numerical accuracy than the required accuracy in computer graphics. In computer graphics, we often need to direct and adjust physical features, or even to bend the reality, to meet artistic goals, which is a key factor that makes computer graphics distinct from physical sciences. However, pure physically-based simulations evolve their solutions according to pre-set physics rules that are notoriously difficult to control. In order to face these challenges we have developed a new lightweight physically-based cloudsimulationschemethatsimulatesthedynamicpropertiesofcloudformation. This new model avoids solving the physically-based equations typically used to simulate the formation of clouds by explicitly solving these equations using SkewT/LogP thermodynamic diagrams. The system incorporates a weather model that uses real data to simulate parameters related to cloud formation. This is specially suitable to the simulation of cumulus clouds, which result from a convective process. This approach not only reduces the computational costs of previous physically-based methods, but also provides a technique to control the shape and dynamics of clouds by handling the cloud levels in SkewT/LogP diagrams. In this thesis, we have also tackled a new challenge, which is related to the simulation oforographic clouds. From ourknowledge, this isthefirstattempttosimulatethis type of cloud formation. The novelty in this method relates to the fact that these clouds are non-convective, so that different atmospheric levels have to be determined. Moreover, since orographic clouds form over mountains, we have also to determine the mountain influence in the cloud motion. In summary, this thesis presents a set of algorithms for the modelling and simulation of cumulus and orographic clouds, taking advantage of the SkewT/LogP diagrams for the first time in the field of computer graphics

High Energy Astrophysics Program

This report reviews activities performed by members of the USRA (Universities Space Research Association) contract team during the six months during the reporting period (10/95 - 3/96) and projected activities during the coming six months. Activities take place at the Goddard Space Flight Center, within the Laboratory for High Energy Astrophysics. Developments concern instrumentation, observation, data analysis, and theoretical work in Astrophysics. Missions supported include: Advanced Satellite for Cosmology and Astrophysics (ASCA), X-ray Timing Experiment (XTE), X-ray Spectrometer (XRS), Astro-E, High Energy Astrophysics Science, Archive Research Center (HEASARC), and others