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.