Best Genetic Algorithms Books of 2025

The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil is a fascinating exploration of the future of human development and the merging of human and machine. Kurzweil argues that the exponential growth of technology will lead to the union of human and machine, resulting in a world where the knowledge and skills of our creations will be combined with our own. The book is well-researched and thought-provoking, filled with imaginative and scientifically grounded speculation. While not all of Kurzweil's predictions may come to pass, the book is still worth reading for its wealth of information and important insights into the future of artificial intelligence.

Elements of Programming Interviews in Python is a comprehensive guide to interviewing for software development roles. It includes a collection of over 250 problems with detailed solutions, representative of interview questions asked at leading software companies. The book covers nontechnical aspects of interviewing, such as strategies for a great interview and tips on negotiating the best offer. Additionally, it provides a summary of data structures, algorithms, and problem-solving patterns. This book is a practical, fun approach to computer science fundamentals, making it a valuable resource for anyone preparing for software development interviews.

Deep Learning by John Kelleher is an excellent introduction to the fundamental technology behind artificial intelligence. The book explains how deep learning enables data-driven decisions and how it extracts patterns from large datasets. Kelleher also covers the basic concepts in deep learning, the most important deep learning architectures, and the two fundamental algorithms in deep learning. Moreover, the book discusses the current state of the art and the future of deep learning. Overall, this book is an accessible and concise but comprehensive guide to the essential knowledge of deep learning.

Impractical Python Projects is a book that offers a collection of fun and educational projects designed to entertain programmers while enhancing their Python skills. The author expands on existing concepts and introduces new tools, making it a great "next book" for those who want to become better coders. The book includes projects with a zany twist featuring historical incidents, pop culture references, and literary allusions. This book is a must-read for anyone who is looking to pick up some new Python skills while having endless educational, geeky fun.

Ideas That Created the Future: Classic Papers of Computer Science is a collection of 46 classic papers in computer science that documents the discoveries and inventions that created today's digital world. The book covers all aspects of computer science, including theory, practice, architectures, algorithms, and logic. It emphasizes the period of 1936-1980 but also includes important early work. The book is accompanied by brief essays by Harry Lewis, the editor, offering historical and intellectual context. This book is a must-read for anyone interested in the evolution of computer science and its impact on our world.

This book, titled "Feature Extraction, Construction and Selection: A Data Mining Perspective," provides an overview of the state-of-the-art techniques in feature extraction, construction, and selection. It compiles contributions from leading researchers in this field and serves as an essential guide for practitioners in statistics, pattern recognition, and data mining. The book aims to increase awareness of these techniques, which are currently conducted in isolation, and to pave the way for developing meta-systems and novel approaches. This book is a valuable resource for those looking to enhance their data mining capabilities.

Complex Adaptive Systems: An Introduction to Computational Models of Social Life provides a detailed introduction to the concepts of complex adaptive social systems with intuitive explanations. The book covers key tools and ideas that have emerged in the field since the mid-1990s, and the techniques needed to investigate such systems. John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. Overall, the book is a highly readable and insightful guide to the field of computational modeling of social life.

This book, titled "Computational Thinking (The MIT Press Essential Knowledge series)", offers an accessible overview of computational thinking (CT). The authors explain that CT is not a set of concepts for programming, but rather a way of thinking honed through practice. They identify six dimensions of today's highly developed CT and cover each in a chapter. The book debunks inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity. It is an informative read for those interested in the history and development of computational thinking.

This comprehensive introduction to machine learning techniques and algorithms covers newer approaches, including deep learning and auto-encoding, as well as Bayesian classifiers, artificial neural networks, and the genetic algorithm. The book is written in an easy-to-understand manner with practical advice and discussions of simple applications. It also devotes special attention to performance evaluation, statistical assessment, and practical issues like feature selection and feature construction. Overall, this book is an excellent resource for anyone looking to learn about machine learning and its applications in the field of artificial intelligence.

The book "Feature Engineering and Selection" offers a comprehensive guide to finding the best representations of predictors for modeling and improving model performance by selecting the best subset of predictors. The authors use various example datasets and provide R programs to reproduce the results. The book is a timely and needed addition to the statistics literature, and it is expected to become popular as a textbook, self-study material, and reference. The book's unique focus on feature engineering and selection makes it an essential read for data scientists and machine learning practitioners.