A definitive account of the origins of modern mathematical population genetics, first published in 2000.

An accessible but rigorous treatment of the theoretical foundations of population genetics. Population genetics—the branch of evolutionary biology concerned with understanding how and why populations’ genetic compositions change over time—rests on a well-developed theoretical foundation that draws on genetics, mathematics, and computer science. This textbook provides an approachable but rigorous treatment for advanced undergraduate and graduate students interested in building a quantitative understanding of the genetics of evolution. Existing texts either assume very mathematically advanced readers, or avoid much of the underlying theory, instead focusing on current methods of data analysis. In contrast, The Foundations of Population Genetics develops the theory from first principles. Requiring only confidence in algebra, this self-contained, student-friendly book illustrates the conceptual framework, terminology, and methods of mathematical modeling. It progressively introduces concepts from genetics as needed, while emphasizing biological implications throughout. As a result, readers come away with a deep understanding of the structure of population genetics without needing to master its mathematics. Connects theory with the most recent genetic data better than existing texts Features engaging real-world examples and extensive original figures Provides dozens of carefully scaffolded questions that deepen the reader's understanding of key concepts Ideal as a succinct reference for established scientists in biology, medicine, and computer science Instructor resources available

This is the first of a planned two-volume work discussing the mathematical aspects of population genetics with an emphasis on evolutionary theory. This volume draws heavily from the author’s 1979 classic, but it has been revised and expanded to include recent topics which follow naturally from the treatment in the earlier edition, such as the theory of molecular population genetics.

No leading university department of mathematics or statistics, or library, can afford to be without this unique text. Leading authorities give a unique insight into a wide range of currently topical problems, from the mathematics of road networks to the genomics of cancer.

The genetic model; Two alleles at a single locus; Two alleles using homogeneous coordinates; Many alleles at a single locus; The special case of three alleles; An X-linked locus; Miscellaneous single-locus models.

Very Good,No Highlights or Markup,all pages are intact.

Foundations of Genetic Algorithms, Volume 2 provides insight of theoretical work in genetic algorithms. This book provides a general understanding of a canonical genetic algorithm. Organized into six parts encompassing 19 chapters, this volume begins with an overview of genetic algorithms in the broader adaptive systems context. This text then reviews some results in mathematical genetics that use probability distributions to characterize the effects of recombination on multiple loci in the absence of selection. Other chapters examine the static building block hypothesis (SBBH), which is the underlying assumption used to define deception. This book discusses as well the effect of noise on the quality of convergence of genetic algorithms. The final chapter deals with the primary goal in machine learning and artificial intelligence, which is to dynamically and automatically decompose problems into simpler problems to facilitate their solution. This book is a valuable resource for theorists and genetic algorithm researchers.

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This book draws together some mathematical ideas that are useful in population genetics, concentrating on a few aspects which are both biologically relevant and mathematically interesting.