The Best New Genetic Algorithms Books To Read In 2025

Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP, and XAI using Python libraries.

Key FeaturesLearn how to implement genetic algorithms using Python libraries DEAP, scikit-learn, and NumPyTake advantage of cloud computing technology to increase the performance of your solutionsDiscover bio-inspired algorithms such as particle swarm optimization (PSO) and NEATPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWritten by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms. After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications. By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python. What you will learnUse genetic algorithms to solve planning, scheduling, gaming, and analytics problemsCreate reinforcement learning, NLP, and explainable AI applicationsEnhance the performance of ML models and optimize deep learning architectureDeploy genetic algorithms using client-server architectures, enhancing scalability and computational efficiencyExplore how images can be reconstructed using a set of semi-transparent shapesDelve into topics like elitism, niching, and multiplicity in genetic solutions to enhance optimization strategies and solution diversityWho this book is forIf you’re a data scientist, software developer, AI enthusiast who wants to break into the world of genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. Working knowledge of the Python programming language is required to get started with this book. Table of ContentsAn Introduction to Genetic AlgorithmsUnderstanding the Key Components of Genetic AlgorithmsUsing the DEAP FrameworkCombinatorial OptimizationConstraint SatisfactionOptimizing Continuous FunctionsEnhancing Machine Learning Models Using Feature SelectionHyperparameter Tuning Machine Learning ModelsArchitecture Optimization of Deep Learning NetworksReinforcement Learning with Genetic AlgorithmsNatural Language ProcessingExplainable AI and CounterfactualsSpeeding Up Genetic Algorithms with ConcurrencyHarnessing the CloudGenetic Image ReconstructionOther Evolutionary and Bio-Inspired Computation Techniques.

Embark on an exhilarating journey into the world of genetic algorithms with our all-encompassing special report, "Unlocking Genetic Algorithms: From Foundations to Real-World Applications." This guidebook is meticulously designed to refresh seasoned tech enthusiasts or enlighten spirited novices. Why genetic algorithms? These fascinating structures, modeled after principles of biological evolution, are revolutionizing computing and problem-solving methodologies. They're transforming industries, reshaping scientific research, and altering our understanding of how solutions to complex problems can be found. What's inside?

Decoding the Basics of Genetic AlgorithmsUnderstanding the Building Blocks: Chromosomes, Genes, and Fitness FunctionsStudying the Mechanics: Selection, Crossover, and MutationExploring Significant Genetic Algorithm VariantsImplementing Genetic Algorithms: A Practical GuideApplications in Real-world: Engaging Case StudiesOur guide is not a mere exposition of genetic algorithms; it is a meticulous analysis of the practical application of these innovative techniques. It widens your horizons to how these magnificent constructs are being used to crack open previously insurmountable problems. Authored by technophile Keith Tate, noted for his knack at communicating complex concepts with charming simplicity, our special report isn't just a mere information repository. It's an exciting narrative—thought-provoking, captivating, and above all, enlightening. Come, navigate the circuitous alleys of genetic algorithms with us! Ready to redefine your perspective on computing technologies?

Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. In Evolutionary Deep Learning you will learn how to:.

Solve complex design and analysis problems with evolutionary computationTune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimizationUse unsupervised learning with a deep learning autoencoder to regenerate sample dataUnderstand the basics of reinforcement learning and the Q-Learning equationApply Q-Learning to deep learning to produce deep reinforcement learningOptimize the loss function and network architecture of unsupervised autoencodersMake an evolutionary agent that can play an OpenAI Gym game. Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. In this one-of-a-kind guide, you’ll discover tools for optimizing everything from data collection to your network architecture. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology. Deep learning meets evolutionary biology in this incredible book. Explore how biology-inspired algorithms and intuitions amplify the power of neural networks to solve tricky search, optimization, and control problems. Relevant, practical, and extremely interesting examples demonstrate how ancient lessons from the natural world are shaping the cutting edge of data science. About the book. Evolutionary Deep Learning introduces evolutionary computation (EC) and gives you a toolbox of techniques you can apply throughout the deep learning pipeline. Discover genetic algorithms and EC approaches to network topology, generative modeling, reinforcement learning, and more! Interactive Colab notebooks give you an opportunity to experiment as you explore. What's inside. Solve complex design and analysis problems with evolutionary computationTune deep learning hyperparametersApply Q-Learning to deep learning to produce deep reinforcement learningOptimize the loss function and network architecture of unsupervised autoencodersMake an evolutionary agent that can play an OpenAI Gym game. About the reader. For data scientists who know Python. About the author. Micheal Lanham is a proven software and tech innovator with over 20 years of experience. Table of Contents. PART 1 - GETTING STARTED. 1 Introducing evolutionary deep learning. 2 Introducing evolutionary computation. 3 Introducing genetic algorithms with DEAP. 4 More evolutionary computation with DEAP. PART 2 - OPTIMIZING DEEP LEARNING. 5 Automating hyperparameter optimization. 6 Neuroevolution optimization. 7 Evolutionary convolutional neural networks. PART 3 - ADVANCED APPLICATIONS. 8 Evolving autoencoders. 9 Generative deep learning and evolution. 10 NEAT: NeuroEvolution of Augmenting Topologies. 11 Evolutionary learning with NEAT. 12 Evolutionary machine learning and beyond.