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

A Novel Small Molecule Inhibitor of Hepatitis C Virus Entry

Small molecule inhibitors of hepatitis C virus (HCV) are being developed to complement or replace treatments with pegylated interferons and ribavirin, which have poor response rates and significant side effects. Resistance to these inhibitors emerges rapidly in the clinic, suggesting that successful therapy will involve combination therapy with multiple inhibitors of different targets. The entry process of HCV into hepatocytes represents another series of potential targets for therapeutic intervention, involving viral structural proteins that have not been extensively explored due to experimental limitations. To discover HCV entry inhibitors, we utilized HCV pseudoparticles (HCVpp) incorporating E1-E2 envelope proteins from a genotype 1b clinical isolate. Screening of a small molecule library identified a potent HCV-specific triazine inhibitor, EI-1. A series of HCVpp with E1-E2 sequences from various HCV isolates was used to show activity against all genotype 1a and 1b HCVpp tested, with median EC50 values of 0.134 and 0.027 µM, respectively. Time-of-addition experiments demonstrated a block in HCVpp entry, downstream of initial attachment to the cell surface, and prior to or concomitant with bafilomycin inhibition of endosomal acidification. EI-1 was equally active against cell-culture adapted HCV (HCVcc), blocking both cell-free entry and cell-to-cell transmission of virus. HCVcc with high-level resistance to EI-1 was selected by sequential passage in the presence of inhibitor, and resistance was shown to be conferred by changes to residue 719 in the carboxy-terminal transmembrane anchor region of E2, implicating this envelope protein in EI-1 susceptibility. Combinations of EI-1 with interferon, or inhibitors of NS3 or NS5A, resulted in additive to synergistic activity. These results suggest that inhibitors of HCV entry could be added to replication inhibitors and interferons already in development

Exact GP Schema Theory for Headless Chicken Crossover and Subtree Mutation

Here a new general GP schema theory for headless chicken crossover and subtree mutation is presented. The theory gives an exact formulation for the expected number of instances of a schema at the next generation either in terms of microscopic quantities or in terms of macroscopic ones. The paper gives examples which show how the theory can be specialised to specific operators

Emergent Behaviour, Population-based Search and Low-pass Filtering

In recent work we have formulated a model of emergent coordinated behaviour for a population of interacting entities. The model is a modified spring mass model where the masses can perceive the environment and generate external forces. As a result of the interactions the population behaves like a single organism moving under the e#ect the vector sum of the external forces generated by each entity. When such forces are proportional to the gradient of a resource distribution f(x), the resultant force controlling the single emergent organism is proportional to the gradient of a modified food distribution. This is the result of applying a filtering kernel to f(x)

Surgeon volume impacts hospital mortality for pancreatic resection

OBJECTIVE: Improved outcomes after pancreatic resection (PR) by high volume (HV) surgeons have been reported in single center studies, which may be confounded with potential selection and referral bias. We attempted to determine if improved outcomes by HV surgeons are reproducible when patient demographic factors are controlled at the population level. METHODS: Using the Nationwide Inpatient Sample, discharge records with surgeon identifiers for all nontrauma PR (n = 3581) were examined from 1998 to 2005. Surgeons were divided into 2 groups: (HV; \u3e or = 5 operations/year) or low volume (LV; \u3c5 operations\u3e/year). We created a logistic regression model to examine the relationship between surgeon type and operative mortality while accounting for patient and hospital factors. To further eliminate differences in cohorts and determine the true effect of surgeon volume on mortality, case-control groups based on patient demographics were created using propensity scores. RESULTS: One hundred thirty-four HV and 1450 LV surgeons performed 3581 PR in 742 hospitals across 12 states that reported surgeon identifier information over the 8-year period. Patients who underwent PR by HV surgeons were more likely to be male, white raced, and a resident of a high-income zip code (P \u3c 0.05). Significant independent factors for in-hospital mortality after PR included increasing age, male gender, Medicaid insurance, and surgery by HV surgeon. HV surgeons had a lower adjusted mortality compared with LV surgeons (2.4% vs. 6.4%; P \u3c 0.0001). CONCLUSIONS: After controlling for patient demographics and factors, pancreatic resection by a HV surgeon in this case-controlled cohort was independently associated with a 51% reduction in in-hospital mortality