Grammar-based Representation and Identification of Dynamical Systems

In this paper we propose a novel approach to identify dynamical systems. The method estimates the model structure and the parameters of the model simultaneously, automating the critical decisions involved in identification such as model structure and complexity selection. In order to solve the combined model structure and model parameter estimation problem, a new representation of dynamical systems is proposed. The proposed representation is based on Tree Adjoining Grammar, a formalism that was developed from linguistic considerations. Using the proposed representation, the identification problem can be interpreted as a multi-objective optimization problem and we propose a Evolutionary Algorithm-based approach to solve the problem. A benchmark example is used to demonstrate the proposed approach. The results were found to be comparable to that obtained by state-of-the-art non-linear system identification methods, without making use of knowledge of the system description.Comment: Submitted to European Control Conference (ECC) 201

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

Using genetic algorithms to create meaningful poetic text

Work carried out when all authors were at the University of Edinburgh.Peer reviewedPostprin

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

An Implementation of a Flexible Author-Reviewer Model of Generation using Genetic Algorithms

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

Adaptive Operator Mechanism for Genetic Programming

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. Robert Ian McKay.their performances are competitive with systems without an adaptive operator mechanism. However they showed some drawbacks, which we discuss. To overcome them, we suggest three variants on operator selection, which performed somewhat better. We have investigated evaluation of operator impact in adaptive operator mechanism, which measures the impact of operator applications on improvement of solution. Hence the impact guides operator rates, evaluation of operator impact is very important in adaptive operator mechanism. There are two issues in evaluation of operator impact: the resource and the method. Basically all history information of run are able to be used as resources for the operator impact, but fitness value which is directly related with the improvement of solution, is usually used as a resource. By using a variety of problems, we used two kinds of resources: accuracy and structure in this thesis. On the other hand, although we used same resources, the evaluated impacts are different by methods. We suggested several methods of the evaluation of operator impact. Although they require only small change, they have a large effect on performance. Finally, we verified adaptive operator mechanism by applying it to a real-world applicationa modeling of algal blooms in the Nakdong River. The objective of this application is a model that describes and predicts the ecosystem of the Nakdong River. We verified it with two researches: fitting the parameters of an expert-derived model for the Nakdong River with a GA, and modeling by extending the expert-derived model with TAG3P.유전 프로그래밍은 모델 학습에 효과적인 진화 연산 알고리즘이다. 유전 프로그래밍은 다양한 파라미터를 가지고 있는데, 이들 파라미터의 값은 대체로 주어진 문제에 맞춰 사용자가 직접 조정한다. 유전 프로그래밍의 성능은 파라미터의 값에 따라 크게 좌우되기 때문에 파라미터 설정에 대한 연구는 진화 연산에서 많은 주목을 받고 있다. 하지만 아직까지 효과적으로 파라미터를 설정하는 방법에 대한 보편적인 지침이 없으며, 많은 실험을 통한 시행착오를 거치면서 적절한 파라미터 값을 찾는 방법이 일반적으로 쓰이고 있다. 본 논문에서 제시하는 적응 연산자 메커니즘은 여러 파라미터 중 유전 연산자의 적용률을 설정해 주는 방법으로, 학습 중간중간의 상황에 맞춰 연산자 적용률을 자동적으로 조정한다. 본 논문에서는, 기존의 적응 연산자 방법을 다양한 유전 연산자를 가진 문법 기반의 유전 프로그래밍인 TAG3P에 적용하고 새로운 적응 연산자 방법을 개발함으로써, 적응 연산자 메커니즘의 적용 범위를 유전 프로그래밍 영역까지 확장하였다. 기존의 적응 연산자 알고리즘을 TAG3P에 적용시키는 연구는 성공적으로 이루어졌으나 몇 가지 문제점을 드러내었다. 이 문제점은 본문에서 후술한다. 이 문제점을 해결하기 위해 유전자 선택에 대한 새로운 변형 알고리즘을 제시하였고, 이는 기존 알고리즘과 비교하여 더 좋은 성능을 보여주었다. 한편으로 유전 연산자가 해의 향상에 미치는 영향을 측정하는 연산자 영향력 평가에 대한 연구도 진행하였다. 적응 연산자 메커니즘에서는 측정된 영향력을 바탕으로 연산자의 적용률을 변화시키기 때문에 영향력 평가는 적응 연산자 메커니즘에서 매우 중요하다. 이 연구에서는 어떤 정보를 이용하여 영향력을 측정할 것인지, 그리고 어떤 방법을 이용하여 영향력을 측정할 것인지의 두 가지 주요 쟁점을 다룬다. 연산자 영향력 평가에는 학습 과정의 모든 정보가 사용될 수 있으며, 대체로 해의 향상과 직접적인 관련이 있는 적합도를 이용한다. 본 논문에서는 다양한 문제를 이용하여 정확도와 구조에 관련된 두 지표를 영향력 평가에 이용해보았다. 한편으로 같은 정보를 이용하더라도 그것을 활용하는 방법에 따라 측정되는 영향력이 달라지는데, 본 논문에서는 작은 변화를 통해서도 큰 성능 변화를 야기시킬 수 있는 영향력 평가 방법을 몇가지 소개한다. 마지막으로 적응 연산자 메커니즘을 실제 문제에 적용함으로써 유용성을 확인하였다. 이를 위해 사용된 실제 문제는 낙동강의 녹조 현상에 대한 예측으로, 낙동강의 생태 시스템을 묘사하고 예측하는 모델을 개발하는 것을 목적으로 한다. 2가지 연구를 통해 유용성을 확인하였다. 우선 전문가에 의해 만들어진 기본 모델을 바탕으로, 유전 알고리즘을 이용하여 모델의 파라미터를 최적화 하였고, 그리고 TAG3P를 이용하여 기본 모델의 확장하고 이를 통해 새로운 모델을 만들어 보았다.Genetic programming (GP) is an effective evolutionary algorithm for many problems, especially suited to model learning. GP has many parameters, usually defined by the user according to the problem. The performance of GP is sensitive to their values. Parameter setting has been a major focus of study in evolutionary computation. However there is still no general guideline for choosing efficient settings. The usual method for parameter setting is trial and error. The method used in this thesis, adaptive operator mechanism, replaces the user's action in setting rates of application of genetic operators. adaptive operator mechanism autonomously controls the genetic operators during a run. This thesis extends adaptive operator mechanism to genetic programming, applying existing adaptive operator algorithms and developing them for TAG3P, a grammar-guided GP which supports a wide variety of useful genetic operators. Existing adaptive operator selection algorithms are successfully applied to TAG3P1 Introduction 1 1.1 Background and Motivation 1 1.2 Our Approach and Its Contributions 2 1.3 Outline 4 2 Related Works 5 2.1 Evolutionary Algorithms 5 2.1.1 Genetic Algorithm 5 2.1.2 Genetic Programming 8 2.1.3 Tree Adjoining Grammar based Genetic Programming 9 3 Adaptive Mechanism and Adaptive Operator Selection 16 3.1 Adaptive Mechanism 16 3.2 Adaptive Operator Selection 18 3.2.1 Operator Selection 18 3.2.2 Evaluation of Operator Impact 19 3.3 Algorithms of Adaptive Operator Selection 20 3.3.1 Probability Matching 21 3.3.2 Adaptive Pursuit 22 3.3.3 Multi-Armed Bandits 25 4 Preliminary Experiment for Adaptive Operator Mechanism 28 4.1 Test Problems 28 4.2 Experimental Design 30 4.2.1 Search Space 31 4.2.2 General Parameter Settings 32 4.3 Results and Discussion 34 5 Operator Selection 39 5.1 Operator Selection Algorithms for GP 39 5.1.1 Powered Probability Matching 39 5.1.2 Adaptive Probability Matching 41 5.1.3 Recursive Adaptive Pursuit 41 5.2 Experiments and Results 43 5.2.1 Test Problems 43 5.2.2 Experimental Design 44 5.2.3 Results and Discussion 46 6 Evaluation of Operator Impact 56 6.1 Rates for the Amount of Individual Usage 57 6.1.1 Denition of Rates for the Amount of Individual Usage 57 6.1.2 Results and Discussion 58 6.2 Ratio for the Improvement of Fitness 63 6.2.1 Pairs and Group 64 6.2.2 Ratio and Children Fitness 65 6.2.3 Experimental Design 65 6.2.4 Result and Discussion 66 6.3 Ranking Point 73 6.3.1 Denition of Ranking Point 73 6.3.2 Experimental Design 74 6.3.3 Result and Discussion 74 6.4 Pre-Search Structure 76 6.4.1 Denition of Pre-Search Structure 76 6.4.2 Preliminary Experiment for Sampling 78 6.4.3 Experimental Design 82 6.4.4 Result and Discussion 83 7 Application: Nakdong River Modeling 85 7.1 Problem Description 85 7.1.1 Outline 85 7.1.2 Data Description 86 7.1.3 Model Description 88 7.1.4 Methods 93 7.2 Results 97 7.2.1 Parameter Optimization 97 7.2.2 Modeling 101 7.3 Summary 103 8 Conclusion 104 8.1 Summary 104 8.2 Future Works 108Docto

An Empirical Study of Graph Grammar Evolution

Vukovar, Croati