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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.
This is the Solutions manual for Introduction to Genetic Analysis.
The author presents a basic introduction to the world of genetic engineering. Copyright © Libri GmbH. All rights reserved.
Authoritative, thorough, and engaging, Life: The Science of Biology achieves an optimal balance of scholarship and teachability, never losing sight of either the science or the student. The first introductory text to present biological concepts through the research that revealed them, Life covers the full range of topics with an integrated experimental focus that flows naturally from the narrative. This approach helps to bring the drama of classic and cutting-edge research to the classroom - but always in the context of reinforcing core ideas and the innovative scientific thinking behind them. Students will experience biology not just as a litany of facts or a highlight reel of experiments, but as a rich, coherent discipline.
From the publisher. Since its inception, Introduction to Genetic Analysis (IGA) has been known for its prominent authorship including leading scientists in their field who are great educators. This market best-seller exposes students to the landmark experiments in genetics, teaching students how to analyze experimental data and how to draw their own conclusions based on scientific thinking while teaching students how to think like geneticists.
Who is a Native American? And who gets to decide? From genealogists searching online for their ancestors to fortune hunters hoping for a slice of casino profits from wealthy tribes, the answers to these seemingly straightforward questions have profound ramifications. The rise of DNA testing has further complicated the issues and raised the stakes. In Native American DNA, Kim TallBear shows how DNA testing is a powerful—and problematic—scientific process that is useful in determining close biological relatives. But tribal membership is a legal category that has developed in dependence on certain social understandings and historical contexts, a set of concepts that entangles genetic information in a web of family relations, reservation histories, tribal rules, and government regulations. At a larger level, TallBear asserts, the “markers” that are identified and applied to specific groups such as Native American tribes bear the imprints of the cultural, racial, ethnic, national, and even tribal misinterpretations of the humans who study them. TallBear notes that ideas about racial science, which informed white definitions of tribes in the nineteenth century, are unfortunately being revived in twenty-first-century laboratories. Because today’s science seems so compelling, increasing numbers of Native Americans have begun to believe their own metaphors: “in our blood” is giving way to “in our DNA.” This rhetorical drift, she argues, has significant consequences, and ultimately she shows how Native American claims to land, resources, and sovereignty that have taken generations to ratify may be seriously—and permanently—undermined.
With Genetics: A Conceptual Approach, Pierce brings a master teacher's experiences to the introductory genetics textbook, clarifying this complex subject by focusing on the big picture of genetics concepts. The new edition features an emphasis on problem-solving and relevant applications, while incorporating the latest trends in genetics research.
It has been recognized for almost 200 years that certain families seem to inherit cancer. It is only in the past decade, however, that molecular genetics and epidemiology have combined to define the role of inheritance in cancer more clearly, and to identify some of the genes involved. The causative genes can be tracked through cancer-prone families via genetic linkage and positional cloning. Several of the genes discovered have subsequently been proved to play critical roles in normal growth and development. There are also implications for the families themselves in terms of genetic testing with its attendant dilemmas, if it is not clear that useful action will result. The chapters in The Genetics of Cancer illustrate what has already been achieved and take a critical look at the future directions of this research and its potential clinical applications.
After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.