{"product_id":"9789819219971","title":"Evolving Insights: Genetic Programming for Symbolic Regression Generalisation and Interpretability","description":"\u003ch3\u003eGenetic and Evolutionary Computation\u003c\/h3\u003e\u003ch1\u003eEvolving Insights: Genetic Programming for Symbolic Regression\u003c\/h1\u003e\u003ch2\u003eGeneralisation and Interpretability\u003c\/h2\u003e\u003ch3\u003eQi Chen | Bing Xue | Mengjie Zhang\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Artificial Intelligence \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp style=\"text-align: justify;\"\u003eThis 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.\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify;\"\u003eA 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.\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify;\"\u003eDesigned 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 \u003cem\u003eEvolving Insights\u003c\/em\u003e an essential resource for anyone seeking clarity and trust in machine learning.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cstrong\u003eQi Chen\u003c\/strong\u003e is a Senior Lecturer in Artificial Intelligence at Victoria University of Wellington, New Zealand. Her research expertise lies in genetic programming, symbolic regression, evolutionary computation, and explainable AI, with a strong emphasis on model interpretability and generalization. Dr. Chen has published extensively in top-tier journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics, ECJ and GPEM, and has presented her work at major conferences such as GECCO, PPSN, CEC and Evo Star. She is an active reviewer and program committee member for leading venues in evolutionary computation and machine learning. Dr. Chen also leads research projects supporting PhD.\u003c\/p\u003e\r\n\u003cp\u003e\u003cstrong\u003eBing Xue\u003c\/strong\u003e is a Professor of Artificial Intelligence at Victoria University of Wellington, New Zealand, a Fellow of IEEE, and a Fellow of Engineering New Zealand. Her research focuses on evolutionary computation, symbolic regression, feature selection, multi-objective optimization, and automated machine learning. She has authored over 500 peer-reviewed publications, including in top-tier journals such as IEEE Transactions on Evolutionary Computation (TEVC), IEEE Transactions on Cybernetics (TCYB), ECJ, and Information Sciences. Professor Xue is an Associate Editor for leading journals such as IEEE TEVC and ACM Transactions on Evolutionary Learning and Optimization, and regularly serves as Conference\/Program Chair or Senior PC Member for major international conferences, including EuroGP, ACM GECCO, IEEE CEC, and PRICAI. She is also a Clarivate Reveals Highly Cited Researcher 2023 and 2024.\u003c\/p\u003e\r\n\u003cp\u003e\u003cstrong\u003eMengjie Zhang\u003c\/strong\u003e is a Professor of Computer Science at Victoria University of Wellington, New Zealand. He is a Fellow of the Royal Society of New Zealand, a Fellow of IEEE, and a Fellow of Engineering New Zealand. Professor Zhang is internationally recognized for his influential work in genetic programming, evolutionary computation, evolutionary machine learning, symbolic regression, feature selection, and image analysis. He has authored over 800 peer-reviewed publications in top venues such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, and GECCO. He received the “Evo* Award for Outstanding Contribution to Evolutionary Computation in Europe 2023” and the “2024 Australasian Artificial Intelligence Distinguished Research Contribution Award”. He is also a Clarivate Reveals Highly Cited Researcher 2023 and 2024.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e21 August 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Singapore\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9789819219971\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e274\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Singapore","offers":[{"title":"Default Title","offer_id":49938600132748,"sku":"9789819219971","price":197.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9789819219971.jpg?v=1781057225","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9789819219971","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}