Best Genetic Algorithms Books
Genetic programming is one of the most interesting aspects of machine learning and AI, where computer programs are encoded as a set of genes that are then modified (evolved) using an evolutionary algorithm.
1. Elements of Programming Interviews in Python: The Insiders' Guide
Author: by Adnan Aziz
Published at: CreateSpace Independent Publishing Platform (September 15, 2016)
This is the Python version of our book. See the website for links to the C++ and Java version.Have you ever… Wanted to work at an exciting futuristic company? Struggled with an interview problem thatcould have been solved in 15 minutes?
Wished you could study real-world computing problems? If so, you need to read Elements of Programming Interviews (EPI). EPI is your comprehensive guide to interviewing for software development roles. The core of EPI is a collection of over 250 problems with detailed solutions.
The problems are representative of interview questions asked at leading software companies. The problems are illustrated with 200 figures, 300 tested programs, and 150 additional variants. The book begins with a summary of the nontechnical aspects of interviewing, such as strategies for a great interview, common mistakes, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI.
We also provide a summary of data structures, algorithms, and problem solving patterns. Coding problems are presented through a series of chapters on basic and advanced data structures, searching, sorting, algorithm design principles, and concurrency. Each chapter stars with a brief introduction, a case study, top tips, and a review of the most important library methods.
2. Password Book: Internet Password Organizer: 6" x 9" Small Password Journal and Alphabetical Tabs | Password Logbook | Logbook To Protect Usernames
Author: by Ink Designs
Published at: Independently published (October 19, 2019)
Are you tired of get remember the usernames and passwords you created every time you visit a website? The password book is designed to keep all your important website addresses, usernames and passwords in a secoure and convenient location. The pages are in alphabetical tabs so you can find easily and quickly find what you’re looking.
Features:Plenty of space: 105 pagesAlphabetized tabsPremium glossy-finished cover designPerfectly sized at 6 x 9Printed on high qualityIt is also a great gift idea for:Birthday GiftsChristmas GiftsMeeting New Friends GiftsBFF GiftsFamily GiftsAnd much more. Scroll to the top of the page and click the buy button now.
3. The Singularity Is Near: When Humans Transcend Biology
Author: by Ray Kurzweil
Published at: Penguin Books (September 26, 2006)
Classic papers by thinkers ranging from from Aristotle and Leibniz to Norbert Wiener and Gordon Moore that chart the evolution of computer science. Ideas That Created the Future collects forty-six classic papers in computer science that map the evolution of the field.
It covers all aspects of computer science: theory and practice, architectures and algorithms, and logic and software systems, with an emphasis on the period of 1936-1980 but also including important early work. Offering papers by thinkers ranging from Aristotle and Leibniz to Alan Turing and Nobert Wiener, the book documents the discoveries and inventions that created today’s digital world.
Each paper is accompanied by a brief essay by Harry Lewis, the volume’s editor, offering historical and intellectual context.
5. Deep Learning (The MIT Press Essential Knowledge series)
Author: by John D. Kelleher
Published at: The MIT Press; Illustrated edition (September 10, 2019)
An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications.
When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.
Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power.
6. Impractical Python Projects: Playful Programming Activities to Make You Smarter
Author: by Lee Vaughan
Published at: No Starch Press; Illustrated edition (November 27, 2018)
Impractical Python Projects is a collection of fun and educational projects designed to entertain programmers while enhancing their Python skills. It picks up where the complete beginner books leave off, expanding on existing concepts and introducing new tools that you’ll use every day.
And to keep things interesting, each project includes a zany twist featuring historical incidents, pop culture references, and literary allusions. You’ll flex your problem-solving skills and employ Python’s many useful libraries to do things like: Help James Bond crack a high-tech safe with a hill-climbing algorithm – Write haiku poems using Markov Chain Analysis- Use genetic algorithms to breed a race of gigantic rats- Crack the world’s most successful military cipher using cryptanalysis- Derive the anagram, “I am Lord Voldemort” using linguistical sieves – Plan your parents’ secure retirement with Monte Carlo simulation- Save the sorceress Zatanna from a stabby death using palingrams- Model the Milky Way and calculate our odds of detecting alien civilizations- Help the world’s smartest woman win the Monty Hall problem argument- Reveal Jupiter’s Great Red Spot using optical stacking- Save the head of Mary, Queen of Scots with steganography- Foil corporate security with invisible electronic inkSimulate volcanoes, map Mars, and more, all while gaining valuable experience using free modules like Tkinter, matplotlib, Cprofile, Pylint, Pygame, Pillow, and Python-Docx.
7. Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series)
Author: by Max Kuhn
Published at: Chapman and Hall/CRC; 1st edition (August 2, 2019)
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance.
A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
8. Password Book With Alphabetical Tabs: Internet Address & Password Organizer Logbook Small (110 Pages, 5.5 x 8.5 in)
Author: by Walter Publishing
Published at: Independently published (January 25, 2020)
Record all your passwords in one place – no need to try to remember where you wrote down a password! There are an alphabetical tab printed on every page, so you can section your password easily. An great notebook for entering passwords from all websites you use.
The Pages are arranged in alphabetical order, so you can easily and quickly find what you are looking. Keep your online passwords in one book for easy reference. Get your copy nowThis password tracking journal features: Cover Finish: Matte Dimensions: Print 5.5″ x 8.
5″ size – small enough to take with you (13.97 x 21. 59 cm)Interior: White PaperPages: 110
9. Computational Thinking (The MIT Press Essential Knowledge series)
Author: by Peter J. Denning
Published at: The MIT Press; Illustrated edition (May 14, 2019)
An introduction to computational thinking that traces a genealogy beginning centuries before the digital computer. A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation; eventually, every field had a computational branch: computational physics, computational biology, computational sociology.
More recently, computational thinking has become part of the K12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview, tracing a genealogy that begins centuries before digital computers and portraying computational thinking as pioneers of computing have described it.
The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes.
10. My Password Book: Internet Address Organizer | Password Logbook | Internet Password Book with Tabs | Password Booklet
Author: by Rooi Planners
Published at: Independently published (July 5, 2020)
Keep all your usernames and passwords in one convenient place! Internet Password Book with Tabs in Alphabetical Order Password keeper book to record Internet addresses, usernames, passwords, and notes for 1,000 Websites Find records quickly with convenient alphabetical tabs from A to Z More pages for Alphabet letters that are frequently used as the first letter for websites Small password booklet that is easy to carry 5.5 x 8.
5 inches 102 pages Gloss laminated cover
11. Turing's Vision: The Birth of Computer Science (The MIT Press)
Author: by Chris Bernhardt
Published at: The MIT Press; Reprint edition (April 21, 2017)
Turing’s fascinating and remarkable theory, which now forms the basis of computer science, explained for the general reader. In 1936, when he was just twenty-four years old, Alan Turing wrote a remarkable paper in which he outlined the theory of computation, laying out the ideas that underlie all modern computers.
This groundbreaking and powerful theory now forms the basis of computer science. In Turing’s Vision, Chris Bernhardt explains the theory, Turing’s most important contribution, for the general reader. Bernhardt argues that the strength of Turing’s theory is its simplicity, and that, explained in a straightforward manner, it is eminently understandable by the nonspecialist.
As Marvin Minsky writes, The sheer simplicity of the theory’s foundation and extraordinary short path from this foundation to its logical and surprising conclusions give the theory a mathematical beauty that alone guarantees it a permanent place in computer theory.
Bernhardt begins with the foundation and systematically builds to the surprising conclusions. He also views Turing’s theory in the context of mathematical history, other views of computation (including those of Alonzo Church), Turing’s later work, and the birth of the modern computer.
13. Evolutionary Optimization Algorithms
Author: by Dan Simon
Published at: Wiley; 1st edition (April 29, 2013)
A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies.
This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clearbut theoretically rigorousunderstanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAsincluding opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author’s website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.
14. An Introduction to Computational Learning Theory (The MIT Press)
Author: by Michael J. Kearns
Published at: The MIT Press (August 15, 1994)
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.
Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting.
Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs.
15. Spatial Computing (The MIT Press Essential Knowledge series)
Author: by Shashi Shekhar
Published at: The MIT Press (February 18, 2020)
An accessible guide to the ideas and technologies underlying such applications as GPS, Google Maps, Pokmon Go, ride-sharing, driverless cars, and drone surveillance. Billions of people around the globe use various applications of spatial computing dailyby using a ride-sharing app, GPS, the e911 system, social media check-ins, even Pokmon Go.
Scientists and researchers use spatial computing to track diseases, map the bottom of the oceans, chart the behavior of endangered species, and create election maps in real time. Drones and driverless cars use a variety of spatial computing technologies. Spatial computing works by understanding the physical world, knowing and communicating our relation to places in that world, and navigating through those places.
It has changed our lives and infrastructures profoundly, marking a significant shift in how we make our way in the world. This volume in the MIT Essential Knowledge series explains the technologies and ideas behind spatial computing. The book offers accessible descriptions of GPS and location-based services, including the use of Wi-Fi, Bluetooth, and RFID for position determination out of satellite range; remote sensing, which uses satellite and aerial platforms to monitor such varied phenomena as global food production, the effects of climate change, and subsurface natural resources on other planets; geographic information systems (GIS), which store, analyze, and visualize spatial data; spatial databases, which store multiple forms of spatial data; and spatial statistics and spatial data science, used to analyze location-related data.
16. Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions (Pragmatic Programmers)
Author: by Frances Buontempo
Published at: Pragmatic Bookshelf; 1st edition (February 12, 2019)
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics.
If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.
In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels.