This book explores the power of genetic programming for symbolic regression (GPSR), offering a unique pathway to discovering models that are both highly accurate and naturally interpretable. With a clear focus on the twin goals of generalisation and interpretability, the book introduces cutting-edge techniques such as representation learning, complexity control, semantic-aware operators, and multi-objective optimisation for GPSR. Each chapter blends foundational theory with empirical investigation, guiding readers through model development, evaluation, and experiments on a variety of scientific and engineering datasets.A central theme of this book is generalisation—the ability of a model to perform well on new, unseen data. Unlike traditional symbolic regression methods that risk overfitting, the techniques presented in this book aim to evolve models that not only fit the training data but also maintain performance on new, independent data. Readers will explore principled approaches to regularisation, semantic diversity preservation, and fitness evaluations to promote generalisation, all designed to build models that are robust and reliable in practical settings.Designed for researchers, data scientists, and research students, this book provides practical tools to evolve symbolic models that are interpretable, trustworthy, and effective in capturing meaningful patterns in data. Readers will benefit from structured frameworks for building interpretable models, proven strategies to reduce overfitting and improve robustness, and insights into model interpretability. Engaging case studies and examples throughout the book bring these methods to life, making Evolving Insights an essential resource for anyone seeking clarity and trust in machine learning.

This book explores the power of genetic programming for symbolic regression (GPSR), offering a unique pathway to discovering models that are both highly accurate and naturally interpretable. With a clear focus on the twin goals of generalisation and interpretability, the book introduces cutting-edge techniques such as representation learning, complexity control, semantic-aware operators, and multi-objective optimisation for GPSR. Each chapter blends foundational theory with empirical investigation, guiding readers through model development, evaluation, and experiments on a variety of scientific and engineering datasets.A central theme of this book is generalisation—the ability of a model to perform well on new, unseen data. Unlike traditional symbolic regression methods that risk overfitting, the techniques presented in this book aim to evolve models that not only fit the training data but also maintain performance on new, independent data. Readers will explore principled approaches to regularisation, semantic diversity preservation, and fitness evaluations to promote generalisation, all designed to build models that are robust and reliable in practical settings.Designed for researchers, data scientists, and research students, this book provides practical tools to evolve symbolic models that are interpretable, trustworthy, and effective in capturing meaningful patterns in data. Readers will benefit from structured frameworks for building interpretable models, proven strategies to reduce overfitting and improve robustness, and insights into model interpretability. Engaging case studies and examples throughout the book bring these methods to life, making Evolving Insights an essential resource for anyone seeking clarity and trust in machine learning.