Dan Simon – författare

A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear—but theoretically rigorous—understanding of evolutionary algorithms, with an emphasis on implementationGives a careful treatment of recently developed EAs—including opposition-based learning, artificial fish swarms, bacterial foraging, and many others— and discusses their similarities and differences from more well-established EAsIncludes chapter-end problems plus a solutions manual available online for instructorsOffers simple examples that provide the reader with an intuitive understanding of the theoryFeatures source code for the examples available on the author's websiteProvides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modelingEvolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

A bottom-up approach that enables readers to master and apply the latest techniques in state estimationThis book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering.While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimationSimple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practiceMATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parametersArmed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering.Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.

The criminal justice process is unavoidably human. Police detectives, witnesses, suspects, and victims shape the course of investigations, while prosecutors, defense attorneys, jurors, and judges affect the outcome of adjudication. In this sweeping review of psychological research, Dan Simon shows how flawed investigations can produce erroneous evidence and why well-meaning juries send innocent people to prison and set the guilty free. The investigator’s task is genuinely difficult and prone to bias. This often leads investigators to draw faulty conclusions, assess suspects’ truthfulness incorrectly, and conduct coercive interrogations that can lead to false confessions. Eyewitnesses’ identification of perpetrators and detailed recollections of criminal events rely on cognitive processes that are often mistaken and can easily be skewed by the investigative procedures used. In the courtroom, jurors and judges are ill-equipped to assess the accuracy of testimony, especially in the face of the heavy-handed rhetoric and strong emotions that crimes arouse. Simon offers an array of feasible ways to improve the accuracy of criminal investigations and trials. While the limitations of human cognition will always be an obstacle, these reforms can enhance the criminal justice system’s ability to decide correctly whom to release and whom to punish.

Mediators are often pulled in many directions—they want to help their clients reach a speedy agreement, ensure the agreement is fair, and avoid coercion so they can honor mediation’s primary value of party self-determination. Can we have it all?In this groundbreaking resource, Dan Simon and Tara West illustrate how self-determination can mean much more than the absence of coercion—it can mean the opportunity for participants to increase their sense of agency as they gain clarity and confidence to make their own decisions, including those that express their highest values. Offering psychological research, philosophical principles, and real-life mediation stories, the authors examine where self-determination belongs in relation to other values, such as fairness, protection, and efficiency, as they wrestle with how to apply their principles in particularly challenging divorces, workplace conflicts, and more. Readers will be challenged to think deeply about how their values and assumptions guide their practice, and they will be inspired to more fully embrace their commitment to self-determination.

Mediators are often pulled in many directions—they want to help their clients reach a speedy agreement, ensure the agreement is fair, and avoid coercion so they can honor mediation’s primary value of party self-determination. Can we have it all?In this groundbreaking resource, Dan Simon and Tara West illustrate how self-determination can mean much more than the absence of coercion—it can mean the opportunity for participants to increase their sense of agency as they gain clarity and confidence to make their own decisions, including those that express their highest values. Offering psychological research, philosophical principles, and real-life mediation stories, the authors examine where self-determination belongs in relation to other values, such as fairness, protection, and efficiency, as they wrestle with how to apply their principles in particularly challenging divorces, workplace conflicts, and more. Readers will be challenged to think deeply about how their values and assumptions guide their practice, and they will be inspired to more fully embrace their commitment to self-determination.

Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.