The Language of Physics: The Calculus and the Development of Theoretical Physics in Europe, 1750 - 1870 Review

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The Language of Physics: The Calculus and the Development of Theoretical Physics in Europe, 1750 - 1870 ReviewFor the first time Garber makes sense out of the threads which came together at the end of the nineteenth century to create modern theoretical physics.
Beginning in the late eighteenth century, scientists working on the development of differential and integral calculus showed how a group of bodies possessed invariant qualities such as kinetic energy, action, and potential energy.
Until Gerber's work, these scientists -- Bernoulli, Mayer, Lagrange, Laplace, Poisson, and Jacobi, among others -- were celebrated as doing great work in physics.
Their work did have consequences for physics, and implied quite a bit from the point of view of mathematical treatment of physical phenomena, but their intention had been to develop the boundaries of mathematics, specifically the differential equations of calculus.
Using detailed examples from not only mechanics but electromagnetism and thermodynamics as well, Garber revises the old-fashioned view of the development of theoretical physics, and proves how the work of a large number of her colleagues in the history of 18th- and 19th-century mathematics and physics supports her interpretation.
A major step forward in the history of science.The Language of Physics: The Calculus and the Development of Theoretical Physics in Europe, 1750 - 1870 OverviewThis work is the first explicit examination of the key role that mathematics has played in the development of theoretical physics and will undoubtedly challenge the more conventional accounts of its historical development. Although mathematics has long been regarded as the "language" of physics, the connections between these independent disciplines have been far more complex and intimate than previous narratives have shown. The author convincingly demonstrates that practices, methods, and language shaped the development of the field, and are a key to understanding the mergence of the modern academic discipline. Mathematicians and physicists, as well as historians of both disciplines, will find this provocative work of great interest.

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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) Review

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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) ReviewThis is a good book to learn about Estimation of Distribution Algorithms (EDAs or also called DEAs or Iterated DEAs). These algorithms are similar to evolutionary algorithms, but do not use the crossover or mutation operators of evolutionary search. EDAs instead create a probabilistic model of good solutions and use the model to generate new search points. It's a nifty idea and it works.
Most of the chapters of this edited collection were authored or coauthored by the editors. So, algorithms developed by other people do not get a lot of attention. However, the editors (or is it the authors) manage to include chapters on combinatorial, continuous, and discrete optimization.
There is a section on machine learning applications that is OK, but the last chapter on training neural nets with EDAs is very weak (look ma I used this and it worked...). Except for this chapter, the rest of the chapters in this section use careful experiments and statistics to make their points.
Making the source code available would have improved things and would make it easier for people to try these algorithms.Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) OverviewEstimation of Distribution Algorithms: A New Tool forEvolutionary Computation is devoted to a new paradigm forevolutionary computation, named estimation of distribution algorithms(EDAs). This new class of algorithms generalizes genetic algorithms byreplacing the crossover and mutation operators with learning andsampling from the probability distribution of the best individuals ofthe population at each iteration of the algorithm. Working in such away, the relationships between the variables involved in the problemdomain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of thetechniques and applications of this new tool for performingevolutionary computation. Estimation of Distribution Algorithms: ANew Tool for Evolutionary Computation is clearly divided intothree parts. Part I is dedicated to the foundations of EDAs. In thispart, after introducing some probabilistic graphical models -Bayesian and Gaussian networks - a review of existing EDAapproaches is presented, as well as some new methods based on moreflexible probabilistic graphical models. A mathematical modeling ofdiscrete EDAs is also presented. Part II covers several applicationsof EDAs in some classical optimization problems: the travellingsalesman problem, the job scheduling problem, and the knapsackproblem. EDAs are also applied to the optimization of some well-knowncombinatorial and continuous functions. Part III presents theapplication of EDAs to solve some problems that arise in the machinelearning field: feature subset selection, feature weighting inK-NN classifiers, rule induction, partial abductive inferencein Bayesian networks, partitional clustering, and the search foroptimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool forEvolutionary Computation is a useful and interesting tool forresearchers working in the field of evolutionary computation and forengineers who face real-world optimization problems. This book mayalso be used by graduate students and researchers in computer science.`... I urge those who are interested in EDAs to study thiswell-crafted book today.' David E. Goldberg, University ofIllinois Champaign-Urbana.

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