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

Automatic control program creation using concurrent Evolutionary Computing

Over the past decade, Genetic Programming (GP) has been the subject of a significant amount of research, but this has resulted in the solution of few complex real -world problems. In this work, I propose that, for some relatively simple, non safety -critical embedded control applications, GP can be used as a practical alternative to software developed by humans. Embedded control software has become a branch of software engineering with distinct temporal, interface and resource constraints and requirements. This results in a characteristic software structure, and by examining this, the effective decomposition of an overall problem into a number of smaller, simpler problems is performed. It is this type of problem amelioration that is suggested as a method whereby certain real -world problems may be rendered into a soluble form suitable for GP. In the course of this research, the body of published GP literature was examined and the most important changes to the original GP technique of Koza are noted; particular focus is made upon GP techniques involving an element of concurrency -which is central to this work. This search highlighted few applications of GP for the creation of software for complex, real -world problems -this was especially true in the case of multi thread, multi output solutions. To demonstrate this Idea, a concurrent Linear GP (LGP) system was built that creates a multiple input -multiple output solution using a custom low -level evolutionary language set, combining both continuous and Boolean data types. The system uses a multi -tasking model to evolve and execute the required LGP code for each system output using separate populations: Two example problems -a simple fridge controller and a more complex washing machine controller are described, and the problems encountered and overcome during the successful solution of these problems, are detailed. The operation of the complete, evolved washing machine controller is simulated using a graphical LabVIEWapplication. The aim of this research is to propose a general purpose system for the automatic creation of control software for use in a range of problems from the target problem class -without requiring any system tuning: In order to assess the system search performance sensitivity, experiments were performed using various population and LGP string sizes; the experimental data collected was also used to examine the utility of abandoning stalled searches and restarting. This work is significant because it identifies a realistic application of GP that can ease the burden of finite human software design resources, whilst capitalising on accelerating computing potential

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

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On Counterinsurgency: Firepower, Biopower, and the Collateralization of Milliatry Violence

This dissertation investigates the most recent cycle of North Atlantic expeditionary warfare by addressing the resuscitation of counterinsurgency warfare with a specific focus on the war in Afghanistan from 2006 to 2014. The project interrogates the lasting aesthetic, epistemological, philosophical, and territorial implications of counterinsurgency, which should be understood as part of wider transformations in military affairs in relation to discourses of adaptation, complexity, and systemic design, and to the repertoire of global contingency and stability operations. Afghanistan served as a counterinsurgency laboratory, and the experiments will shape the conduct of future wars, domestic security practices, and the increasingly indistinct boundary between them. Using work from Michel Foucault and liberal war studies, the project undertakes a genealogy of contemporary population-centred counterinsurgency and interrogates how its conduct is constituted by and as a mixture firepower and biopower. Insofar as this mix employs force with different speeds, doses, and intensities, the dissertation argues that counterinsurgency unrestricts and collateralizes violence, which is emblematic of liberal war that kills selectively to secure and make life live in ways amenable to local and global imperatives of liberal rule. Contemporary military counterinsurgents, in conducting operations on the edges of liberal rule's jurisdiction and in recursively influencing the domestic spaces of North Atlantic states, fashion biopoweras custodial power to conduct the conduct of lifeto shape different interventions into the everyday lives of target populations. The 'lesser evil' logic of counterinsurgency is used to frame counterinsurgency as a type of warfare that is comparatively low-intensity and less harmful, and this justification actually lowers the threshold for violence by making increasingly indiscriminate the ways in which its employment damages and envelops populations and communities, thereby allowing counterinsurgents to speculate on the practice of expeditionary warfare and efforts to sustain occupations. Thus, the dissertation argues that counterinsurgency is a communicative process, better understood as mobile military media with an atmospheric-environmental register blending acute and ambient measures that are always-already kinetic. The counterinsurgent gaze enframes a world picture where everything can be a force amplifier and everywhere is a possible theatre of operations

Exceptional scale: metafiction and the maximalist tradition in contemporary American literary history

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Chasing shadow and make believe

My thesis is a science fiction novella. It follows the story of an adolescent boy, Shadow, and a little girl, Makebelieve, in an ahistorical future. The world that they traverse is earth, after being nursed back to health, by technologically advanced Southern African societies. A series of inexplicable astronomical events leads to their being hunted down. Through the travels of Shadow and Makebelieve I show how the world and the societies around them operate. I am inspired by Samuel R Delaney’s Aye, Gomorrah and Derrick Bell’s The Space Traders, because of their prowess in world building and exploration of complex and innovative ideas

Plenum Life: Formality in the Movement of Free Ecology

This work centres on a specific functional moment in the creation of collective self-defense: a novel reconceptualization of the endurance (and the negotiations around endurance) of forms that protect the conditions for emergence. Thus this thesis seeks to forward and invent theories and techniques of technological-financial-governmental activation that self-dispossess the ‘source code’ in these fields so activated, informed by the black radical tradition, process philosophy, and interaction-as-computation category theory. It seeks this forwarding by articulating a new role for formality in the world through the perversion of the discourse of the philosophy of engineering (one already happening, partially, within itself). This thesis is interested in the hypothesis that (1) there is nothing proprietary in informality’s production of the social economic conditions associated with it, (2) formality may then play a role in such productions, augmenting key functional aspects, such as the option for endurance, and adding to the field its own expressive inventions, not only without hampering or dislodging what Denise Ferreira da Silva calls the poethical, but perhaps revealing how it participates in the poethical, and (3) that an articulation of such a role will reveal new frameworks for organizational design tout court, frames able to ripple through practices and disciplines previously thought to be discrete and siloed

In the Colonies

In the Colonies is a work of fiction. It tells the story of a young German harpist, C––, who is seduced into a life of luxury by a venal American, Sansone. She is invited to spend a year at his artists’ colony, where she works on composing a transcendent work of music and, in the process, realizes that she has lost sight of the material realities around her. Ultimately, she comes to realize that her single-minded pursuit of an ideal Beauty has driven her away from the very ideals she aspired to in the first place