Genetic Roulette

Argues against the biotech industry's claim that genetically modified (GM) foods are safe, identifying sixty-five health risks of the foods that Americans eat every day, and showing how official safety assessments on GM crops are not competent to identify the health problems involved, and how industry research is rigged to avoid finding problems.

Without knowing it, Americans eat genetically modified (GM) food every day. While the food and chemical industries claim that GMO food is safe, a considerable amount of evidence shows otherwise. In Seeds of Deception, Jeffrey Smith, a former executive with the leading independent laboratory testing for GM presence in foods, documents these serious health dangers and explains how corporate influence and government collusion have been used to cover them up. The stories Smith presents read like a mystery novel. Scientists are offered bribes or threatened; evidence is stolen; data withheld or distorted. Government scientists who complain are stripped of responsibilities or fired. The FDA even withheld information from congress after a GM food supplement killed nearly a hundred people and permanently disabled thousands. While Smith was employed by the laboratory he was not allowed to speak on the health dangers or the cover-up. No longer bound by this agreement, Smith now reveals what he knows in this groundbreaking expose. Today, food companies sell GM foods that have not undergone safety studies. FDA scientists opposed this, but White House and industry pressure prevailed and the agency's final policy--co-authored by a former Monsanto attorney--denied the risks. The scientists' concerns were made public only after a lawsuit forced the agency to turn over internal documents. Dan Glickman, former Secretary of Agriculture, describes the government's pro-biotech mindset: "You felt like you were almost an alien, disloyal, by trying to present an open-minded view. . . . So I pretty much spouted the rhetoric. . . . It was written into my speeches." In Seeds of Deception Smith offers easy-to-understand descriptions of genetic engineering and explains why it can result in serious health problems. This well-documented, pivotal work will show you how to protect yourself and your family. DVD Overview Three videos in one: includes an interview with Jeffrey M. Smith, footage of scientists, and a look at the miraculous improvement in student behavior that accompanied a change in diet at a Wisconsin school. Also included is a lecture by Smith on "The Health Dangers of Genetically Engineered Foods and Their Cover-up."

The patriarch of medical ethics explains why some accepted ethical values need to catch up with the science of human reproduction and why newer reproductive methods can be more "natural" and humane than those they replace.

Offers an exposé on the genetic engineering of foods, maintaining that the unduly reckless way it has been practiced is based, not on sound science, but the subversion of science, and that its promotion has been marked by corruption and the suppression or distortion of facts.

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key FeaturesExplore the ins and outs of genetic algorithms with this fast-paced guideImplement tasks such as feature selection, search optimization, and cluster analysis using PythonSolve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applicationsBook Description Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains. What you will learnUnderstand how to use state-of-the-art Python tools to create genetic algorithm-based applicationsUse genetic algorithms to optimize functions and solve planning and scheduling problemsEnhance the performance of machine learning models and optimize deep learning network architectureApply genetic algorithms to reinforcement learning tasks using OpenAI GymExplore how images can be reconstructed using a set of semi-transparent shapesDiscover other bio-inspired techniques, such as genetic programming and particle swarm optimizationWho this book is for This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.

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. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.

The Generalized Nets (GNs) are extensions of Petri nets and of different Petri nets modifications, introduced by the author (1982). In the book, definitions and the basic properties of GNs are given. The GNs extensions and reductions are discussed. GNs, which describe the functioning and results of the work of different types of petri nets, different types of finite automata and of Turing machines, are given. Over the GNs are defined different operations, relations and operators. They can also be transferred onto other nets. Many open problems in the GNs theory are given.