Automatically evolving rule induction algorithms with grammar-based genetic programming

In the last 30 years, research in the field of rule induction algorithms produced a large number of algorithms. However, these algorithms are usually obtained from the combination of a basic rule induction algorithm (typically following the sequential covering approach) with new evaluation functions, pruning methods and stopping criteria for refining or producing rules, generating many "new" and more sophisticated sequential covering algorithms. We cannot deny that these attempts to improve the basic sequential covering approach have succeeded. Hence, if manually changing these major components of rule induction algorithms can result in new, significantly better ones, why not to automate this process to make it more cost-effective? This is the core idea of this work: to automate the process of designing rule induction algorithms by means of grammar-based genetic programming. Grammar-based Genetic Programming (GGP) is a special type of evolutionary algorithm used to automatically evolve computer programs. The most interesting feature of this type of algorithm is that it incorporates a grammar into its search mechanism, which expresses prior knowledge about the problem being solved. Since we have a lot of previous knowledge about how humans design rule induction algorithms, this type of algorithm is intuitively a suitable tool to automatically evolve rule induction algorithms. The grammar given to the proposed GGP system includes knowledge about how humans- design rule induction algorithms, and also presents some new elements which could work in rule induction algorithms, but to the best of our knowledge were not previously tested. The GG P system aims to evolve rule induction algorithms under two different frameworks, as follows. In the first framework, the GGP is used to evolve robust rule induction algorithms, i.e., algorithms which were designed to be applied to virtually any classification data set, like a manually-designed rule induction algorithm. In the second framework, the GGP is applied to evolve rule induction algorithms tailored to a specific application XVI domain, i.e., rule induction algorithms tailored to a single data set. Note that the latter framework is hardly feasible on a hard scale in the case of conventional, manually-designed algorithms, since the number of classification data sets greatly outnumbers the number of rule induction algorithms designers. However, it is clearly feasible on a large scale when using the proposed system, which automates the process of rule induction algorithm design and implementation. Overall, extensive computational experiments with 20 VCI data sets and 5 bioinformatics data sets showed that effective rule induction algorithms can be automatically generated using the GGP in both frameworks. Moreover, the automatically evolved rule induction algorithms were shown to be competitive with (and overall slightly better than) four well-known manually designed rule induction algorithms when comparing their predictive accuracies. The proposed GGP system was also compared to a grammar-based hillclimbing system, and experimental results showed that the GGP system is a more effective method to evolve rule induction algorithms than the grammar-based hillclimbing method. At last, a multi-objective version of the GGP (based on the concept of Pareto dominance) was also proposed, and experiments were performed to evolve robust rule induction algorithms which generate both accurate and simple models. The results showed that in most of the cases the GGP system can produce rule induction algorithms which are competitive in predictive accuracy to wellknown human-designed rule induction algorithms, but generate simpler classification modes - i.e., smaller rule sets, intuitively easier to be interpreted by the user

Automated DNA Motif Discovery

Ensembl's human non-coding and protein coding genes are used to automatically find DNA pattern motifs. The Backus-Naur form (BNF) grammar for regular expressions (RE) is used by genetic programming to ensure the generated strings are legal. The evolved motif suggests the presence of Thymine followed by one or more Adenines etc. early in transcripts indicate a non-protein coding gene. Keywords: pseudogene, short and microRNAs, non-coding transcripts, systems biology, machine learning, Bioinformatics, motif, regular expression, strongly typed genetic programming, context-free grammar.Comment: 12 pages, 2 figure

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

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 Multi-Gene Genetic Programming Application for Predicting Students Failure at School

Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap with arXiv:1403.0623 by other author

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

Ludii -- The Ludemic General Game System

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently proposed Regular Boardgames (RBG) system, and has various advantages in qualitative aspects such as generality.Comment: Accepted at ECAI 202