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 study of genetic programming and grammatical evolution for automatic object-oriented programming.

Master of Science in Computer Science. University of KwaZulu-Natal, Pietermaritzburg 2016.Manual programming is time consuming and challenging for a complex problem. For efficiency of the manual programming process, human programmers adopt the object-oriented approach to programming. Yet, manual programming is still a tedious task. Recently, interest in automatic software production has grown rapidly due to global software demands and technological advancements. This study forms part of a larger initiative on automatic programming to aid manual programming in order to meet these demands. In artificial intelligence, Genetic Programming (GP) is an evolutionary algorithm which searches a program space for a solution program. A program generated by GP is executed to yield a solution to the problem at hand. Grammatical Evolution (GE) is a variation of genetic programming. GE adopts a genotype-phenotype distinction and maps from a genotypic space to a phenotypic (program) space to produce a program. Whereas the previous work on object-oriented programming and GP has involved taking an analogy from object-oriented programming to improve the scalability of genetic programming, this dissertation aims at evaluating GP and a variation thereof, namely, GE, for automatic object-oriented programming. The first objective is to implement and test the abilities of GP to automatically generate code for object-oriented programming problems. The second objective is to implement and test the abilities of GE to automatically generate code for object-oriented programming problems. The third objective is to compare the performance of GP and GE for automatic object-oriented programming. Object-Oriented Genetic Programming (OOGP), a variation of OOGP, namely, Greedy OOGP (GOOGP), and GE approaches to automatic object-oriented programming were implemented. The approaches were tested to produce code for three object-oriented programming problems. Each of the object-oriented programming problems involves two classes, one with the driver program and the Abstract Data Type (ADT) class. The results show that both GP and GE can be used for automatic object-oriented programming. However, it was found that the ability of each of the approaches to automatically generate code for object-oriented programming problems decreases with an increase in the problem complexity. The performance of the approaches were compared and statistically tested to determine the effectiveness of each approach. The results show that GE performs better than GOOGP and OOGP

Automatically Defined Templates for Improved Prediction of Non-stationary, Nonlinear Time Series in Genetic Programming

Soft methods of artificial intelligence are often used in the prediction of non-deterministic time series that cannot be modeled using standard econometric methods. These series, such as occur in finance, often undergo changes to their underlying data generation process resulting in inaccurate approximations or requiring additional human judgment and input in the process, hindering the potential for automated solutions. Genetic programming (GP) is a class of nature-inspired algorithms that aims to evolve a population of computer programs to solve a target problem. GP has been applied to time series prediction in finance and other domains. However, most GP-based approaches to these prediction problems do not consider regime change. This paper introduces two new genetic programming modularity techniques, collectively referred to as automatically defined templates, which better enable prediction of time series involving regime change. These methods, based on earlier established GP modularity techniques, take inspiration from software design patterns and are more closely modeled after the way humans actually develop software. Specifically, a regime detection branch is incorporated into the GP paradigm. Regime specific behavior evolves in a separate program branch, implementing the template method pattern. A system was developed to test, validate, and compare the proposed approach with earlier approaches to GP modularity. Prediction experiments were performed on synthetic time series and on the S&P 500 index. The performance of the proposed approach was evaluated by comparing prediction accuracy with existing methods. One of the two techniques proposed is shown to significantly improve performance of time series prediction in series undergoing regime change. The second proposed technique did not show any improvement and performed generally worse than existing methods or the canonical approaches. The difference in relative performance was shown to be due to a decoupling of reusable modules from the evolving main program population. This observation also explains earlier results regarding the inferior performance of genetic programming techniques using a similar, decoupled approach. Applied to financial time series prediction, the proposed approach beat a buy and hold return on the S&P 500 index as well as the return achieved by other regime aware genetic programming methodologies. No approach tested beat the benchmark return when factoring in transaction costs

Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation

This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference

Evolutionary program induction directed by logic grammars.

by Wong Man Leung.Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 227-236).List of Figures --- p.iiiList of Tables --- p.viChapter Chapter 1 : --- Introduction --- p.1Chapter 1.1. --- Automatic programming and program induction --- p.1Chapter 1.2. --- Motivation --- p.6Chapter 1.3. --- Contributions of the research --- p.8Chapter 1.4. --- Outline of the thesis --- p.11Chapter Chapter 2 : --- An Overview of Evolutionary Algorithms --- p.13Chapter 2.1. --- Evolutionary algorithms --- p.13Chapter 2.2. --- Genetic Algorithms (GAs) --- p.15Chapter 2.2.1. --- The canonical genetic algorithm --- p.16Chapter 2.2.1.1. --- Selection methods --- p.21Chapter 2.2.1.2. --- Recombination methods --- p.24Chapter 2.2.1.3. --- Inversion and Reordering --- p.27Chapter 2.2.2. --- Implicit parallelism and the building block hypothesis --- p.28Chapter 2.2.3. --- Steady state genetic algorithms --- p.32Chapter 2.2.4. --- Hybrid algorithms --- p.33Chapter 2.3. --- Genetic Programming (GP) --- p.34Chapter 2.3.1. --- Introduction to the traditional GP --- p.34Chapter 2.3.2. --- Automatic Defined Function (ADF) --- p.41Chapter 2.3.3. --- Module Acquisition (MA) --- p.44Chapter 2.3.4. --- Strongly Typed Genetic Programming (STGP) --- p.49Chapter 2.4. --- Evolution Strategies (ES) --- p.50Chapter 2.5. --- Evolutionary Programming (EP) --- p.55Chapter Chapter 3 : --- Inductive Logic Programming --- p.59Chapter 3.1. --- Inductive concept learning --- p.59Chapter 3.2. --- Inductive Logic Programming (ILP) --- p.62Chapter 3.2.1. --- Interactive ILP --- p.64Chapter 3.2.2. --- Empirical ILP --- p.65Chapter 3.3. --- Techniques and methods of ILP --- p.67Chapter Chapter 4 : --- Genetic Logic Programming and Applications --- p.74Chapter 4.1. --- Introduction --- p.74Chapter 4.2. --- Representations of logic programs --- p.76Chapter 4.3. --- Crossover of logic programs --- p.81Chapter 4.4. --- Genetic Logic Programming System (GLPS) --- p.87Chapter 4.5. --- Applications --- p.90Chapter 4.5.1. --- The Winston's arch problem --- p.91Chapter 4.5.2. --- The modified Quinlan's network reachability problem --- p.92Chapter 4.5.3. --- The factorial problem --- p.95Chapter Chapter 5 : --- The logic grammars based genetic programming system (LOGENPRO) --- p.100Chapter 5.1. --- Logic grammars --- p.101Chapter 5.2. --- Representations of programs --- p.103Chapter 5.3. --- Crossover of programs --- p.111Chapter 5.4. --- Mutation of programs --- p.126Chapter 5.5. --- The evolution process of LOGENPRO --- p.130Chapter 5.6. --- Discussion --- p.132Chapter Chapter 6 : --- Applications of LOGENPRO --- p.134Chapter 6.1. --- Learning functional programs --- p.134Chapter 6.1.1. --- Learning S-expressions using LOGENPRO --- p.134Chapter 6.1.2. --- The DOT PRODUCT problem --- p.137Chapter 6.1.2. --- Learning sub-functions using explicit knowledge --- p.143Chapter 6.2. --- Learning logic programs --- p.148Chapter 6.2.1. --- Learning logic programs using LOGENPRO --- p.148Chapter 6.2.2. --- The Winston's arch problem --- p.151Chapter 6.2.3. --- The modified Quinlan's network reachability problem --- p.153Chapter 6.2.4. --- The factorial problem --- p.154Chapter 6.2.5. --- Discussion --- p.155Chapter 6.3. --- Learning programs in C --- p.155Chapter Chapter 7 : --- Knowledge Discovery in Databases --- p.159Chapter 7.1. --- Inducing decision trees using LOGENPRO --- p.160Chapter 7.1.1. --- Decision trees --- p.160Chapter 7.1.2. --- Representing decision trees as S-expressions --- p.164Chapter 7.1.3. --- The credit screening problem --- p.166Chapter 7.1.4. --- The experiment --- p.168Chapter 7.2. --- Learning logic program from imperfect data --- p.174Chapter 7.2.1. --- The chess endgame problem --- p.177Chapter 7.2.2. --- The setup of experiments --- p.178Chapter 7.2.3. --- Comparison of LOGENPRO with FOIL --- p.180Chapter 7.2.4. --- Comparison of LOGENPRO with BEAM-FOIL --- p.182Chapter 7.2.5. --- Comparison of LOGENPRO with mFOILl --- p.183Chapter 7.2.6. --- Comparison of LOGENPRO with mFOIL2 --- p.184Chapter 7.2.7. --- Comparison of LOGENPRO with mFOIL3 --- p.185Chapter 7.2.8. --- Comparison of LOGENPRO with mFOIL4 --- p.186Chapter 7.2.9. --- Comparison of LOGENPRO with mFOIL5 --- p.187Chapter 7.2.10. --- Discussion --- p.188Chapter 7.3. --- Learning programs in Fuzzy Prolog --- p.189Chapter Chapter 8 : --- An Adaptive Inductive Logic Programming System --- p.192Chapter 8.1. --- Adaptive Inductive Logic Programming --- p.192Chapter 8.2. --- A generic top-down ILP algorithm --- p.196Chapter 8.3. --- Inducing procedural search biases --- p.200Chapter 8.3.1. --- The evolution process --- p.201Chapter 8.3.2. --- The experimentation setup --- p.202Chapter 8.3.3. --- Fitness calculation --- p.203Chapter 8.4. --- Experimentation and evaluations --- p.204Chapter 8.4.1. --- The member predicate --- p.205Chapter 8.4.2. --- The member predicate in a noisy environment --- p.205Chapter 8.4.3. --- The multiply predicate --- p.206Chapter 8.4.4. --- The uncle predicate --- p.207Chapter 8.5. --- Discussion --- p.208Chapter Chapter 9 : --- Conclusion and Future Work --- p.210Chapter 9.1. --- Conclusion --- p.210Chapter 9.2. --- Future work --- p.217Chapter 9.2.1. --- Applying LOGENPRO to discover knowledge from databases --- p.217Chapter 9.2.2. --- Learning recursive programs --- p.218Chapter 9.2.3. --- Applying LOGENPRO in engineering design --- p.220Chapter 9.2.4. --- Exploiting parallelism of evolutionary algorithms --- p.222Reference --- p.227Appendix A --- p.23

On the definition of non-player character behaviour for real-time simulated virtual environments.

Computer games with complex virtual worlds, which are populated by artificial characters and creatures, are the most visible application of artificial intelligence techniques. In recent years game development has been fuelled by dramatic advances in computer graphics hardware which have led to a rise in the quality of real-time computer graphics and increased realism in computer games. As a result of these developments video games are gaining acceptance and cultural significance as a form of art and popular culture. An important factor for the attainment of realism in games is the artificially intelligent behaviour displayed by the virtual entities that populate the games' virtual worlds. It is our firm belief that to further improve the behaviour of virtual entities, game AI development will have to mirror the advances achieved in game graphics. A major contributing factor for these advancements has been the advent of programmable shaders for real-time graphics, which in turn has been significantly simplified by the introduction of higher level programming languages for the creation of shaders. This has demonstrated that a good system can be vastly improved by the addition of a programming language. This thesis presents a similar (syntactic) approach to the definition of the behaviour of virtual entities in computer games. We introduce the term behaviour definition language (BDL), describing a programming language for the definition of game entity behaviour. We specify the requirements for this type of programming language, which are applied to the development and implementation of several behaviour definition languages, culminating in the design of a new game-genre independent behaviour definition (scripting) language. This extension programming language includes several game AI techniques within a single unified system, allowing the use of different methods of behaviour definition. A subset of the language (itself a BDL) was implemented as a proof of concept of this design, providing a framework for the syntactic definition of the behaviour of virtual entities in computer games

A teachable semi-automatic web information extraction system based on evolved regular expression patterns

This thesis explores Web Information Extraction (WIE) and how it has been used in decision making and to support businesses in their daily operations. The research focuses on a WIE system based on Genetic Programming (GP) with an extensible model to enhance the automatic extractor. This uses a human as a teacher to identify and extract relevant information from the semi-structured HTML webpages. Regular expressions, which have been chosen as the pattern matching tool, are automatically generated based on the training data to provide an improved grammar and lexicon. This particularly benefits the GP system which may need to extend its lexicon in the presence of new tokens in the web pages. These tokens allow the GP method to produce new extraction patterns for new requirements

To what extent can knowledge management systems build and reinforce consensus around initiatives for change?: A self-reflective analysis of professional practice

This thesis reports on my attempts to \u27re-align\u27 the purpose, behaviour and underlying culture of a large military organisation through heuristic, self reflective enquiry - to \u27find its future\u27 - with and through its people. I use the word re-align with great care as I recognised that change would have been too ambitious and would (probably have) result(ed) in failure. Whilst I cannot claim total success, I have made new and valuable discoveries in knowledge elicitation and methods of integrating the views of a large number of people to \u27build and reinforce consensus around initiatives for change\u27. In the process of completing this research I developed a novel approach to strategic planning/policy making that advances the ends, ways and means construct of decision-making into a purer and more refined approach. One that anchors these elements firmly to the organisation and its environment simultaneously through a knowledge management system, enabling the strengths and weaknesses within the organisation to be drawn into sharp focus - an effects based planning approach. I have also fused together the more systematic and disciplined approaches embodied within a knowledge management system with existing and more creative scenario planning/future focussed methods. Thus allowing organisations to undertake \u27self-constructed\u27 audits that have an immediate interest or are situated well into their future, doubling its value as a planning device. As I report, the methods have been presented at the highest levels of Defence, attracting interest from the Australian Minister of Defence. The New Zealand Defence Force, Naval Warfare Development Command of the United States Navy and Australia\u27s Chief of Air Force have also expressed an interest in the potential of an effects-based orientation to planning and policy-making. Whether the concepts and underpinning ideas become established, leading to the discovery of a post-modem military is uncertain. What is clear is that there is a definite move away from a pre-occupation with the means, or the things that are done, towards a more comprehensive understanding of what are we trying to make happen as a guiding principle. This is certainly of value within military \u27organisations and has potential for others involved in complex problem-solving in social settings. A heuristic, self-reflective approach has enriched this search for focussed and \u27change-finding\u27 knowledge, allowing a more purposeful, complete and forthright account of the involvement of others

Constructivist Artificial Intelligence With Genetic Programming

Learning is an essential attribute of an intelligent system. A proper understanding of the process of learning in terms of knowledge-acquisition, processing and its effective use has been one of the main goals of artificial intelligence (AI). AI, in order to achieve the desired flexibility, performance levels and wide applicability should explore and exploit a variety of learning techniques and representations. Evolutionary algorithms, in recent years, have emerged as powerful learning methods employing task-independent approaches to problem solving and are potential candidates for implementing adaptive computational models. These algorithms, due to their attractive features such as implicit and explicit parallelism, can also be powerful meta-leaming tools for other learning systems such as connectionist networks. These networks, also known as artificial neural networks, offer a paradigm for learning at an individual level that provide an extremely rich landscape of learning mechanisms which AI should exploit. The research proposed in this thesis investigates the role of genetic programming (GP) in connectionism, a learning paradigm that, despite being extremely powerful has a number of limitations. The thesis, by systematically identifying the reasons for these limitations has argued as to why connectionism should be approached with a new perspective in order to realize its true potentialities. With genetic-based designs the key issue has been the encoding strategy. That is, how to encode a neural network within a genotype so as to achieve an optimum network structure and/ or an efficient learning that can best solve a given problem. This in turn raises a number of key questions such as: 1. Is the representation (that is the genotype) that the algorithms employ sufficient to express and explore the vast space of network architectures and learning mechanisms? 2. Is the representation capable of capturing the concepts of hierarchy and modularity that are vital and so naturally employed by humans in problem-solving? 3. Are some representations better in expressing these? If so, how to exploit the strengths that are inherent to these representations? 4. If the aim is really to automate the design process what strategies should be employed so as to minimize the involvement of a designer in the design loop? 5. Is the methodology or the approach able to overcome at least some of the limitations that are commonly seen in connectionist networks? 6. Most importantly, how effective is the approach in problem-solving? These issues are investigated through a novel approach that combines genetic programming and a self-organizing neural network which provides a framework for the simulations. Through the powerful notions of constructivism and micro-macro dynamics the approach provides a way of exploiting the potential features (such as the hierarchy and modularity) that are inherent to the representation that GP employs. By providing a general definition for learning and by imposing a single potential constraint within the representation the approach demonstrates that genetic programming, if used for construction and optimization, could be extremely creative. The method also combines the bottom-up and top-down strategies that are key to evolve ALife-like systems. A comparison with earlier methods is drawn to identify the merits of the proposed approach. A pattern recognition task is considered for illustration. Simulations suggest that genetic- programming can be a powerful meta-leaming tool for implementing useful network architectures and flexible learning mechanisms for self-organizing neural networks while interacting with a given task environment. It appears that it is possible to extend the novel approach further to other types of networks. Finally the role of flexible learning in implementing adaptive AI systems is discussed. A number of potential applications domain is identified