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

Gene Expression and its Discontents: Developmental disorders as dysfunctions of epigenetic cognition

Systems biology presently suffers the same mereological and sufficiency fallacies that haunt neural network models of high order cognition. Shifting perspective from the massively parallel space of gene matrix interactions to the grammar/syntax of the time series of expressed phenotypes using a cognitive paradigm permits import of techniques from statistical physics via the homology between information source uncertainty and free energy density. This produces a broad spectrum of possible statistical models of development and its pathologies in which epigenetic regulation and the effects of embedding environment are analogous to a tunable enzyme catalyst. A cognitive paradigm naturally incorporates memory, leading directly to models of epigenetic inheritance, as affected by environmental exposures, in the largest sense. Understanding gene expression, development, and their dysfunctions will require data analysis tools considerably more sophisticated than the present crop of simplistic models abducted from neural network studies or stochastic chemical reaction theory

Leukemia relapse via genetic immune escape after allogeneic hematopoietic cell transplantation

Graft-versus-leukemia (GvL) reactions are responsible for the effectiveness of allogeneic hematopoietic cell transplantation as a treatment modality for myeloid neoplasia, whereby donor T- effector cells recognize leukemia neoantigens. However, a substantial fraction of patients experiences relapses because of the failure of the immunological responses to control leukemic outgrowth. Here, through a broad immunogenetic study, we demonstrate that germline and somatic reduction of human leucocyte antigen (HLA) heterogeneity enhances the risk of leukemic recurrence. We show that preexistent germline-encoded low evolutionary divergence of class II HLA genotypes constitutes an independent factor associated with disease relapse and that acquisition of clonal somatic defects in HLA alleles may lead to escape from GvL control. Both class I and II HLA genes are targeted by somatic mutations as clonal selection factors potentially impairing cellular immune responses and response to immunomodulatory strategies. These findings define key molecular modes of post-transplant leukemia escape contributing to relapse

Cell states along oligodendrocyte development and disease

The brain, one of the most complex organs in the body, where an immense diversity of cell states emerges from simple structure, where function arises from sets of regulatory principles and pattern persist where individual cells do not. Revealing the regulatory underpinnings of the brain, from unspecified cell states to diversity, is paramount for achieving a thorough understanding of the development process and generating insight into the disease states of the brain. This thesis is an exploration into how canonical regulatory factors and elements, such as transcription factors and genes, lock a regulatory system in a multi-outcome network with limited possible states. The work in this thesis focuses on the oligodendrocyte lineage, a glial cell known for it’s supportive role in the central nervous system, where it facilitates electrical transmission through the enscheathment of axons. Oligodendrocytes (OLs) lie at the heart of multiple sclerosis (MS), a disease where an immune response is mounted against myelin. As a response, oligodendrocyte precursor cells (OPCs) move towards lesions and remyelinate axons, however, this mechanism fails in later stages of the disease. Thus, an understanding to how OPCs develop is vital to amelioration of the altered oligodendrocyte population. In Paper I we reveal a previously underestimated heterogeneity within the oligodendrocyte lineage in mouse. We show that OL maturation is an ongoing process, albeit, decreasing in frequency with age. Furthermore, complex wheel training in mice revealed that the OLs respond to this challenge through an increase in differentiation. Paper II investigates the cellular response in the experimental autoimmune encephalomyelitis (EAE) disease mouse model of MS, where we find a tailored response by the resident OL population, changed from its normal transcriptional program, expressing a spectrum of genes related to survival, immunological stimulation, phagocytosis, and active differentiation. Furthermore, we provide evidence that OLs can elicit responses from T cells. In Paper III we explore the different waves of OPC generation in the developing mouse brain at embryonic day 13.5 and postnatal day 7. We show that recently Pdgfra expressing cells at the E13.5 time point exhibit a multitude of patterning genes, and we show the emergence of a possible OPC progenitor through the inclusion of a bridging E17.5 time point population. This pre-OPC population is biased towards expressing glial and OL lineage specifying genes such as Olig1, Olig2, Ptprz1, and Bcan. Furthermore, lineage tracing of OPC developmental waves, shows no transcriptional differences, leading us to conclude that OPCs are generally naïve to the time or region of specification. In Paper IV we show that we are able to detect OPC formation in the developing human forebrain. We detect OPCs at the earliest sampled time point post conception week 8. We attempt to recover the path of OPC formation, and investigate the regulatory dynamics in the specification of OPCs

Novel Computational Methods for the Analysis and Interpretation of MS/MS Data in Metaproteomics

Otto-von-Guericke-Universität Magdeburg, Fakultät für Verfahrens- und Systemtechnik, Dissertation, 2016von Dipl.-Bioinf. Thilo MuthLiteraturverzeichnis: Seite 151-17