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

Languages, machines, and classical computation

3rd ed, 2021. A circumscription of the classical theory of computation building up from the Chomsky hierarchy. With the usual topics in formal language and automata theory

Attribute grammar evolution

The final publication is available at Springer via http://dx.doi.org/10.1007/11499305_19Proceedings of First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Las Palmas, Canary Islands, Spain, June 15-18, 2005This paper describes Attribute Grammar Evolution (AGE), a new Automatic Evolutionary Programming algorithm that extends standard Grammar Evolution (GE) by replacing context-free grammars by attribute grammars. GE only takes into account syntactic restrictions to generate valid individuals. AGE adds semantics to ensure that both semantically and syntactically valid individuals are generated. Attribute grammars make it possible to semantically describe the solution. The paper shows empirically that AGE is as good as GE for a classical problem, and proves that including semantics in the grammar can improve GE performance. An important conclusion is that adding too much semantics can make the search difficult

Spring School on Language, Music, and Cognition: Organizing Events in Time

The interdisciplinary spring school “Language, music, and cognition: Organizing events in time” was held from February 26 to March 2, 2018 at the Institute of Musicology of the University of Cologne. Language, speech, and music as events in time were explored from different perspectives including evolutionary biology, social cognition, developmental psychology, cognitive neuroscience of speech, language, and communication, as well as computational and biological approaches to language and music. There were 10 lectures, 4 workshops, and 1 student poster session. Overall, the spring school investigated language and music as neurocognitive systems and focused on a mechanistic approach exploring the neural substrates underlying musical, linguistic, social, and emotional processes and behaviors. In particular, researchers approached questions concerning cognitive processes, computational procedures, and neural mechanisms underlying the temporal organization of language and music, mainly from two perspectives: one was concerned with syntax or structural representations of language and music as neurocognitive systems (i.e., an intrapersonal perspective), while the other emphasized social interaction and emotions in their communicative function (i.e., an interpersonal perspective). The spring school not only acted as a platform for knowledge transfer and exchange but also generated a number of important research questions as challenges for future investigations

Learning to solve planning problems efficiently by means of genetic programming

Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator - Instance-Based Crossover - that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.Publicad

12th International Workshop on Termination (WST 2012) : WST 2012, February 19–23, 2012, Obergurgl, Austria / ed. by Georg Moser

This volume contains the proceedings of the 12th International Workshop on Termination (WST 2012), to be held February 19–23, 2012 in Obergurgl, Austria. The goal of the Workshop on Termination is to be a venue for presentation and discussion of all topics in and around termination. In this way, the workshop tries to bridge the gaps between different communities interested and active in research in and around termination. The 12th International Workshop on Termination in Obergurgl continues the successful workshops held in St. Andrews (1993), La Bresse (1995), Ede (1997), Dagstuhl (1999), Utrecht (2001), Valencia (2003), Aachen (2004), Seattle (2006), Paris (2007), Leipzig (2009), and Edinburgh (2010). The 12th International Workshop on Termination did welcome contributions on all aspects of termination and complexity analysis. Contributions from the imperative, constraint, functional, and logic programming communities, and papers investigating applications of complexity or termination (for example in program transformation or theorem proving) were particularly welcome. We did receive 18 submissions which all were accepted. Each paper was assigned two reviewers. In addition to these 18 contributed talks, WST 2012, hosts three invited talks by Alexander Krauss, Martin Hofmann, and Fausto Spoto

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