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

Spectra of magnetic perturbations triggered by pellets in JET plasmas

Aiming at investigating edge localised mode (ELM) pacing for future application on ITER, experiments have been conducted on JET injecting pellets in different plasma configurations, including high confinement regimes with type-I and type-III ELMs, low confinement regimes and Ohmically heated plasmas. The magnetic perturbations spectra and the toroidal mode number, n, of triggered events are compared with those of spontaneous ELMs using a wavelet analysis to provide good time resolution of short-lived coherent modes. It is found that—in all these configurations—triggered events have a coherent mode structure, indicating that pellets can trigger an MHD event basically in every background plasma. Two components have been found in the magnetic perturbations induced by pellets, with distinct frequencies and toroidal mode numbers. In high confinement regimes triggered events have similarities with spontaneous ELMs: both are seen to start from low toroidal mode numbers, then the maximum measured n increases up to about 10 within 0.3 ms before the ELM burst

Spin flip scattering at Al surfaces

Non-local measurements are performed on a multi terminal device to $in-situ$ determine the spin diffusion length and in combination with resistivity measurements also the spin relaxation time in Al films. By varying the thickness of Al we determine the contribution to spin relaxation from surface scattering. From the temperature dependence of the spin diffusion length it is established that the spin relaxation is impurity dominated at low temperature. A comparison of the spin and momentum relaxation lengths for different thicknesses reveals that the spin flip scattering at the surfaces is weak compared to that within the bulk of the Al films.Comment: 11 pages, 5 figure

Enhanced spin accumulation in a superconductor

A lateral array of ferromagnetic tunnel junctions is used to inject and detect non-equilibrium quasi-particle spin distribution in a superconducting strip made of Al. The strip width and thickness is kept below the quasi particle spin diffusion length in Al. Non-local measurements in multiple parallel and antiparallel magnetic states of the detectors are used to in-situ determine the quasi-particle spin diffusion length. A very large increase in the spin accumulation in the superconducting state compared to that in the normal state is observed and is attributed to a diminishing of the quasi-particle population by opening of the gap below the transition temperature.Comment: 6 pages, 4 figures; accepted for publication in Journal of Applied Physic

Getting It Right in Obstructive Lung Disease

Chronic hypercapnic respiratory failure in obstructive lung diseases may benefit from nocturnal Home non-invasive ventilation (HNIV). It has been shown that in patients with persistence of hypercapnia after an acute episode of chronic obstructive pulmonary disease (COPD) exacerbation requiring mechanical ventilation, HNIV may improve the risk for new admission and survival. The ability to reach these aims depends on the correct timing of enrolling patients, as well as a correct definition of ventilatory needing and setting of the ventilator. This review tries to define a possible home treatment path of hypercapnic respiratory failure in COPD by analyzing the main studies published in recent years

Getting It Right in Restrictive Lung Disease

Restrictive lung disease (predominantly in patients with neuromuscular disease (NMD) and ribcage deformity) may induce chronic hypercapnic respiratory failure, which represents an absolute indication to start home NIV (HNIV). However, in the early phases of NMD, patients may present only diurnal symptoms or orthopnoea and sleep disturbances with normal diurnal gas exchange. The evaluation of respiratory function decline may predict the presence of sleep disturbances (SD) and nocturnal hypoventilation that can be respectively diagnosed with polygraphy and PCO2 transcutaneous monitoring. If nocturnal hypoventilation and/or apnoea/hypopnea syndrome are detected, HNIV should be introduced. Once HNIV has been started, adequate follow-up is mandatory. The ventilator’s built-in software provides important information about patient adherence and eventual leaks to correct. Detailed data about pressure and flow curves may suggest the presence of upper airway obstruction (UAO) during NIV that may occur with or without decrease in respiratory drive. Etiology and treatment of these two different forms of UAO are different. For this reason, in some circumstances, it might be useful to perform a polygraph. PtCO2 monitoring, together with pulse-oximetry, seem to be very important tools to optimize HNIV. The role of HNIV in neuromuscular disease is to correct diurnal and nocturnal hypoventilation with the consequence of improving quality of life, symptoms, and survival

Spin injection and relaxation in a mesoscopic superconductor

We study spin accumulation and spin relaxation in a superconducting nanowire. Spins are injected and detected by using a set of magnetic tunnel contact electrodes, closely spaced along the nanowire. We observe a giant enhancement of the spin accumulation of up to five orders of magnitude on transition into the superconducting state, consistent with the expected changes in the density of states. The spin relaxation length decreases by an order of magnitude from its value in the normal state. These measurements combined with our theoretical model, allow us to distinguish the individual spin flip mechanisms present in the transport channel. Our conclusion is that magnetic impurities rather than spin-orbit coupling dominate spin-flip scattering in the superconducting state.Comment: 5 pages, 5 figure