{"product_id":"9781041424840","title":"Population-Based Algorithms for Evolutionary and Swarm Intelligence","description":"\u003ch1\u003ePopulation-Based Algorithms for Evolutionary and Swarm Intelligence\u003c\/h1\u003e\u003ch3\u003eJohn W. Sheppard\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Data Science \/ Machine Learning\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003eThis book provides a comprehensive exploration of population-based algorithms through a machine learning lens, focusing on evolutionary and swarm intelligence methods. While the book presents the core algorithms in the field, it also is written to cover topics relevant to difficult optimization problems, such as optimization under uncertainty, optimization in high dimensional spaces, and optimization in dynamic environments. Readers will gain insights into these algorithms by considering issues such as the roles and impacts of representation and preference bias in search, issues around hyperparameter tuning and optimization, design of hybrid methods that incorporate more traditional methods, and population-based methods for solving reinforcement learning (i.e., control and sequential decision making) problems.\u003c\/p\u003e\n\u003cp\u003eThe book is also written from the perspective of guiding a young investigator who is looking to perform research in the field of evolutionary and swarm-based algorithms. Thus, the book serves an essential resource for graduate students, early-career researchers, and academic professionals in computer science, artificial intelligence, and related fields.\u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eApproaches population-based algorithms from the perspective of their relationships to methods in machine learning.\u003c\/li\u003e \u003cli\u003eDiscusses evolutionary algorithms, including genetic algorithms, genetic programming, and differential evolution, with a focus on issues in single and multi-objective optimization.\u003c\/li\u003e \u003cli\u003eCovers swarm-based algorithms such as ant colony optimization and particle swarm optimization, along with alternative swarm intelligence methods.\u003c\/li\u003e \u003cli\u003eExplains theoretical foundations, including convergence properties and computational complexity of population-based algorithms.\u003c\/li\u003e \u003cli\u003eIntroduces advanced topics like co-evolution, combinatorial optimization, self-adaptive algorithms, and hybrid\/memetic approaches.\u003c\/li\u003e \u003cli\u003eExplores applications in reinforcement learning, optimization under uncertainty, and search in dynamic environments.\u003c\/li\u003e \u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003cp\u003eJohn W. Sheppard is a Norm Asbjornson College of Engineering Distinguished Professor in the Gianforte School of Computing at Montana State University. Dr. Sheppard received his BS in computer science (magna cum laude) from Southern Methodist University in Dallas, TX, and both his MS and PhD in computer science from the Johns Hopkins University in Baltimore, MD. Dr. Sheppard has 40 years of experience (20 in industry followed by 20 in academia) where he has performed research in applied and fundamental artificial intelligence and machine learning. From a fundamental point of view, his research interests include distributed optimization through population-based algorithms, reasoning under uncertainty with probabilistic graphical models, deep explainable AI, and the ethical and theological implications of modern machine learning systems. From the applied perspective, he has worked in avionics test, diagnosis, and prognosis, precision agriculture, probabilistic risk assessment for military aircraft and prescribed burns used in fire management, and recommender systems for technical hiring, student cohort design, and scholarly research network analysis. He has over 200 publications in peer-reviewed conferences and journals, as well as four books (including this one). He has been teaching courses in artificial intelligence, machine learning, and population-based algorithms for 30 years and was the designer of two research-focused machine learning courses for Johns Hopkins. He was also the designer of two research-based independent study courses for students in the part time graduate program at Johns Hopkins who were looking for a way to include a research component in a non-thesis graduate degree. Through his academic career, he has graduated 14 PhD students and 24 MS thesis or project students. Professionally, he has been recognized as a Fellow of the Institute for Electrical and Electronics Engineers “for contributions to system-level diagnosis and prognosis.” He has also been a long-time leader in the IEEE Standards Association, chairing several working groups focused on publishing standards related to complex system test and diagnosis. Previously, he also served as the “designated representative” from the IEEE Computer Society to the IEEE Standards Coordinating Committee 20 on Test and Diagnosis for Electronic Systems and as an official delegate to the International Electro-technical Commission’s Technical Committee on Design Automation (TC-93).\u003c\/p\u003e\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e31 December 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eCRC Press\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eCRC Press\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781041424840\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\u003e480\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"CRC Press","offers":[{"title":"Default Title","offer_id":63761409081484,"sku":"9781041424840","price":80.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/lateknightbooks.com\/products\/9781041424840","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}