TY - BOOK ID - 8360906 TI - Biologically-inspired optimisation methods : parallel algorithms, systems and applications AU - Kacprzyk, Janusz. AU - Mostaghim, Sanaz. AU - Randall, Marcus. AU - Lewis, Andrew D. PY - 2009 SN - 3642012612 9786613561688 1280383763 3642012620 PB - Berlin, Heidelberg : Springer Berlin Heidelberg, DB - UniCat KW - Applied Mathematics KW - Civil Engineering KW - Civil & Environmental Engineering KW - Engineering & Applied Sciences KW - Engineering mathematics. KW - Artificial intelligence. KW - Engineering. KW - Construction KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Engineering KW - Engineering analysis KW - Mathematics KW - Applied mathematics. KW - Appl.Mathematics/Computational Methods of Engineering. KW - Artificial Intelligence (incl. Robotics). KW - Industrial arts KW - Technology KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Mathematical analysis KW - Mathematical and Computational Engineering. KW - Artificial Intelligence. UR - https://www.unicat.be/uniCat?func=search&query=sysid:8360906 AB - Humanity has often turned to Nature for inspiration to help it solve its problems. The systems She provides are often based on simple rules and premises, yet are able to adapt to new and complex environments quickly and efficiently. Problems from a range of human endeavours, including, science, engineering and economics, require us to find good quality solutions in exponentially large search spaces, a task that often requires vast amounts computational resources and effort. In this book, the contributing authors solve these problems by modelling aspects of the natural world, from the flocking of birds and fish, the operation of colonies of ants through to chromosome reproduction and beyond. Many of the contributions represent extended studies of work presented at a number of workshops on Biologically-Inspired Optimisation Methods at international conferences on e-Science, Grid Computing, and Evolutionary Computation. A variety of chapters from some of the leading experts in the field present an overview of the state-of-the-art, recent advances in theoretical and practical ideas and techniques, and details of application of these methods to a range of benchmark and real world problems. ER -