4 New Evolutionary Algorithms Books Reshaping AI in 2025

Drawing from over 25 years in software engineering and AI research, Eyal Wirsansky offers a detailed guide to applying genetic algorithms using Python. You’ll learn not just the theory but also how to implement these algorithms with libraries like DEAP and scikit-learn, tackling tasks from optimization and scheduling to reinforcement learning and explainable AI. The book’s chapters on accelerating algorithms with concurrency and cloud computing reveal practical ways to boost performance, while the inclusion of bio-inspired techniques such as NEAT adds depth for advanced users. If you’re comfortable with Python and want to expand your toolkit for solving complex AI problems, this book provides focused, hands-on skills without fluff.

What happens when interdisciplinary research meets evolutionary artificial intelligence? This collection, edited by David Asirvatham and colleagues, presents the latest advances from ICEAI 2023, blending methods like particle swarm optimization, genetic programming, and evolutionary neural networks. You’ll gain insight into hybrid algorithms and emerging bio-inspired techniques that tackle complex computational problems, offering a broad yet detailed overview of current trends. If you’re working on evolutionary computation or interested in innovative algorithmic strategies, this volume situates you at the forefront of ongoing developments with diverse, peer-reviewed contributions.

This tailored book delves into the latest developments and breakthroughs in evolutionary algorithms as they stand in 2025. It explores cutting-edge research, novel algorithmic designs, and emerging applications that shape the current landscape of evolutionary computing. The content is carefully matched to your background and interests, allowing you to focus on the aspects most relevant to your goals and expertise. By engaging with this personalized exploration, you gain a deeper understanding of how evolutionary algorithms continue to evolve and influence AI and machine learning advancements. The book reveals contemporary challenges and innovative solutions that define this dynamic field today.

When Micheal Lanham explores the intersection of evolutionary computation and deep learning, he unlocks novel ways to enhance AI models beyond traditional methods. You learn to apply genetic algorithms and particle swarm optimization to automate tuning of hyperparameters and network architecture, tackling issues like manual adjustment fatigue. The book walks you through creating evolutionary agents capable of playing OpenAI Gym games and optimizing unsupervised autoencoders, offering practical Python-based tools and Colab notebooks for hands-on experimentation. If you're a data scientist eager to push deep learning with biologically inspired algorithms, this book equips you with fresh perspectives and techniques worth integrating into your workflow.

After analyzing numerous cases and recent technological trends, James Daniel developed a detailed guide that navigates from basic principles to the latest advancements in evolutionary algorithms. You’ll explore essential mechanisms like selection, mutation, and recombination, while also grappling with more advanced topics such as neuro-evolution and multi-objective optimization. The book doesn’t just explain theory; it dives into practical challenges like parameter tuning, handling large-scale problems, and ethical considerations in AI applications. If you’re involved in engineering design, machine learning, or fintech and want to understand how evolutionary algorithms can push boundaries, this book lays out the tools and insights you’ll need.