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

Recommendation for an interface system for product related computer data to enhance the Engineering Change Order/Preliminary Change Order function

The following document will explore product and information integration by demonstrating the potential economic, strategic, and technical benefits attainable in the Engineering Change Order/Preliminary Change Order function. Information is the foundation of today\u27s corporate enterprise. An organization\u27s success can depend on how effectively it identifies, manages and uses its information. As an organization grows or becomes more complex, the infrastructure of information becomes more complex. The management and distribution of information corporation wide becomes a key element in the strategic position of the organization in its given market

Rapid change in freshwater content of the Arctic Ocean

Author Posting. © American Geophysical Union, 2009. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 36 (2009): L10602, doi:10.1029/2009GL037525.The dramatic reduction in minimum Arctic sea ice extent in recent years has been accompanied by surprising changes in the thermohaline structure of the Arctic Ocean, with potentially important impact on convection in the North Atlantic and the meridional overturning circulation of the world ocean. Extensive aerial hydrographic surveys carried out in March–April, 2008, indicate major shifts in the amount and distribution of fresh-water content (FWC) when compared with winter climatological values, including substantial freshening on the Pacific side of the Lomonosov Ridge. Measurements in the Canada and Makarov Basins suggest that total FWC there has increased by as much as 8,500 cubic kilometers in the area surveyed, effecting significant changes in the sea-surface dynamic topography, with an increase of about 75% in steric level difference from the Canada to Eurasian Basins, and a major shift in both surface geostrophic currents and freshwater transport in the Beaufort Gyre.Support for this work was provided by the National Science Foundation Office of Polar Programs under grants 0352687, 0634097 (MGM); 0633979, 0806115 (AP); 0633885, 0352754, 0634226 (MAS, JHM); and 06341222 (MBA)

The Scholarship of Critique and Power

Critique can be defined as disciplinary feedback, analysis, or assessment provided to an individual or within a group, be it a classroom or a team. At a fundamental level, it is an exchange of ideas, impressions, evaluations, opinions, reflections, judgments, speculations, or suggestions to oneself or between two or more participants in a defined context. Scholars describe critique as a signature pedagogy in many disciplines, a cornerstone of the educational experience. There has been scant critical analysis of how critique also represents a performance of power with roots in positions of authority, expertise, or assigned roles. Such power dynamics have been explored in some areas within SoTL, for example in scholarship on assessment, epistemic disobedience, social justice, feminist pedagogies, and critical race theory. However, this has generally not been the case within the scholarship on critique. To better understand the dimensions of power in the context of critique we developed a conceptual framework that can be applied at the individual level (teacher to student, student to student) as well as the systemic level (critique as a construct of cultural hegemony in a specific episteme). Drawing from theoretical and pedagogical literature in areas such as cultural studies, whiteness studies, design education, and assessment, the conceptual framework defines power in three main expressions: power as inequity, power as authority, and power as cultural hegemony. The framework can be used to identify and define power within the critique context and to also inform reflection and shift perspectives at various academic levels

Fatigue in adults with cerebral palsy: A three-year follow-up study.

OBJECTIVES: To describe the course of fatigue over a 3-year follow-up period in adults with cerebral palsy and to investigate the association of known determinants of fatigue (i.e. demographic characteristics and/or body composition) with change in fatigue. METHODS: Forty-one adults with cerebral palsy from a previous study of fatigue were invited to participate in a follow-up study. Twenty-three adults with cerebral palsy (Gross Motor Function Classification System (GMFCS) levels I-V; mean age 38 years 2 months, standard deviation (SD) 14 years 1 month)) agreed to participate (convenience sample). Fatigue was measured with the Fatigue Impact and Severity Self-Assessment (FISSA, range 31-157) questionnaire. The course of fatigue is described at group, subgroup (GMFCS) and individual levels. RESULTS: The mean FISSA score for all participants was 84.0 (SD 27.7) at baseline and 91.7 (SD 26.7) at follow-up. Despite variations among individuals in the change of fatigue, there was no statistically significant difference in FISSA score over time (p = 0.087, 95% confidence interval (95% CI) -16.7 to 1.22). No known determinants of fatigue predictive of change in FISSA scores were found. DISCUSSION: Fatigue appears to be relatively stable within adults with cerebral palsy over time, with a variable presentation between individuals and across GMFCS levels. Care providers should monitor and discuss fatigue in young individuals with cerebral palsy in order to attenuate fatigue later in life