Much of the book takes the form of a discussion between a teacher and his students. They propose various solutions to some mathematical problems and investigate the strengths and weaknesses of these solutions. Their discussion (which mirrors certain real developments in the history of mathematics) raises some philosophical problems and some problems about the nature of mathematical discovery or creativity. Imre Lakatos is concerned throughout to combat the classical picture of mathematical development as a steady accumulation of established truths. He shows that mathematics grows instead through a richer, more dramatic process of the successive improvement of creative hypotheses by attempts to 'prove' them and by criticism of these attempts: the logic of proofs and refutations.

    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.

    Ronald Mallett, who recently discovered the basic equations for a working time machine that he believes can be used as a transport vehicle to the past. Combining elements of Rocket Boys and Elegant Universe, Time Traveler follows Mallett's discovery of Einstein's work on space-time, his study of Godel's work on a solution of Einstein's equation that might allow for time travel, and his own research in theoretical physics spanning thirty years that culminated in his recent discovery of the effects of circulating laser light and its application to time travel. The foundation for Mallett's historic time-travel work is Einstein's theory of general relativity, a sound platform for any physicist. Through his years of reading and studying Einstein, Mallett became a buff well before he had any notion of the importance of the grand old relativist's theories to his own career. One interesting subtext to the story is Mallett's identification with, and keen interest in, Einstein. Mallett provides easy-to-understand explanations of the famous physicist's seminal work.

    The ability to duplicate that process in a lab, once thought impossible, may now be closer than we think. Today, teams of scientists around the world are being assembled by the boldest entrepreneurs, big business, and governments to solve what is the most difficult technological challenge humanity has ever faced: building the equivalent of a star on earth. If their plans to capture star power are successful, they will unlock thousands, potentially millions, of years of clean, carbon-free energy. Not only would controlled nuclear fusion help solve the climate crisis, it could also make other highly desired technological ambitions possible—like journeying to the stars. Given the rising alarm over deterioration of the environment, and the strides being made in laser and magnetic field technology, powerful momentum is gathering behind fusion and the possibilities it offers. In The Star Builders, award-winning young plasma physicist Arthur Turrell “offers an optimistic outlook for the future of fusion power and is adamant about the need to invest in it” (The New York Times). Turrell describes fascinating star machines with ten times as many parts as the NASA Space Shuttle, and structures that extend over 400 acres in an accessible and entertaining account, spotlighting the individuals, firms, and institutions racing for the finish line: science-minded entrepreneurs like Jeff Bezos and Peter Thiel, companies like Goldman Sachs and Google, universities like Oxford and MIT, and virtually every rich nation. It’s an exciting and game-changing international quest that will make all of us winners.