Symbolic regression / Gabriel Kronberger, Bogdan Burlacu, Michael Kommenda, Stephen M. Winkler, and Michael Affenzeller.

Format:

Book

Author/Creator:

Kronberger, Gabriel, author.

Burlacu, Bogdan, author.

Kommenda, Michael, author.

Winkler, Stephan M., author.

Affenzeller, Michael, author.

Contributor:

Taylor & Francis eBooks

Language:

English

Subjects (All):

Genetic programming (Computer science).

Machine learning--Mathematics.

Mathematical models.

Logic, Symbolic and mathematical.

Regression analysis.

Engineering mathematics.

Physical Description:

1 online resource (xiii, 293 pages) : illustrations (some color)

Edition:

First edition.

Place of Publication:

Boca Raton, FL : CRC Press, 2025.

Contents:

Cover

Half Title

Title Page

Copyright Page

Contents

Preface

Symbols and Notation

1. Introduction

2. Basics of Supervised Learning

2.1. Introduction

2.2. Regression

2.2.1. Linear Models

2.2.2. Nonlinear Models

2.2.3. Error Measures

2.3. Classification

2.4. Time Series Prediction

2.5. Model Selection

2.6. Cross-validation

2.7. Further Reading

3. Basics of Symbolic Regression

3.1. Example: Identification of a Polynomial

3.1.1. Data Collection and Preprocessing

3.1.2. Establishing a Baseline

3.1.3. Modeling Approach

3.1.4. Modeling Results

3.2. Example: Discovery of Laws of Physics from Data

3.3. Example: Approximation of the Gamma Function

3.4. Extending Symbolic Regression to Classification

3.4.1. Model Structures for Symbolic Classification

3.4.2. Evaluation of Symbolic Classification Models

3.5. Further Reading

4. Evolutionary Computation and Genetic Programming

4.1. General Concepts

4.1.1. Genotype, Phenotype, and Semantics

4.1.2. Diversity and Evolvability

4.1.3. Buffering, Redundancy, and Neutrality

4.2. Population Initialization

4.2.1. Operators

4.3. Fitness Calculation

4.4. Parent Selection

4.4.1. Operators

4.4.2. Selection Pressure

4.5. Bloat and Introns

4.6. Crossover and Mutation

4.7. Power of the Hypothesis Space

4.8. GP Dynamics

4.8.1. Fitness

4.8.2. Variable Relevance

4.8.3. Model Complexity

4.8.4. Diversity

4.9. Algorithmic Extensions

4.9.1. Brood Selection and Offspring Selection

4.9.2. Age-layered Population Structures

4.9.3. Multi-objective GP

4.9.4. Alternative Encodings: Linear and Graph GP

4.9.5. Restricting Expressions: Syntax and Types

4.9.6. Semantics-aware GP

4.10. Conclusions

4.11. Further Reading

5. Model Validation, Inspection, Simplification, and Selection

5.1. Model Validation

5.1.1. Visual Tools

5.1.2. Explaining Models

5.1.3. Model Interpretability

5.2. Model Selection

5.2.1. Criteria for Model Selection

5.2.2. Hold-out Set for Validation

5.2.3. Cross-validation

5.2.4. Akaike's Information Criterion

5.2.5. Bayesian Information Criterion

5.2.6. Minimum Description Length Principle

5.2.7. Comparison of Model Selection Criteria

5.3. Model Simplification

5.3.1. Nested Models

5.3.2. Removal of Subexpressions

5.4. Example: Boston Housing

5.4.1. Data Preprocessing

5.4.2. Model Generation and Selection for Median Values of Homes

5.4.3. Model Generation and Selection for NOX Concentrations

5.5. Conclusions

5.6. Further Reading

6. Advanced Techniques

6.1. Integration of Knowledge

6.1.1. Example Applications

6.1.2. Knowledge Integration Methods

6.1.3. Knowledge Integration via Customized Fitness Evaluation

6.1.4. Shape Constraints

6.1.5. Knowledge Integration via the Hypothesis Space

6.2. Optimization of Coefficients

Notes:

"A Chapman & Hall book" -- title page.

Includes bibliographical references and index.

Electronic reproduction. London Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on October 23, 2024).

Other Format:

Print version: Kronberger, Gabriel. Symbolic regression

ISBN:

9781315166407

1315166402

9780429679421

0429679424

9781351679862

1351679864

9780429679537

042967953X

Publisher Number:

90101469097

Access Restriction:

Restricted for use by site license.