TY - BOOK ID - 4868902 TI - Evolutionary constrained optimization AU - Datta, Rituparna. AU - Deb, Kalyanmoy. PY - 2015 SN - 9788132221845 8132221834 9788132221838 8132221842 PB - New Delhi : Springer India : Imprint: Springer, DB - UniCat KW - Engineering. KW - Computational Intelligence. KW - Artificial Intelligence (incl. Robotics). KW - Mechanical Engineering. KW - Optimization. KW - Artificial intelligence. KW - Mathematical optimization. KW - Mechanical engineering. KW - Ingénierie KW - Intelligence artificielle KW - Optimisation mathématique KW - Génie mécanique KW - Engineering & Applied Sciences KW - Computer Science KW - Constrained optimization. KW - Optimization, Constrained KW - Computational intelligence. KW - Engineering, Mechanical KW - Engineering KW - Machinery KW - Steam engineering KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Optimization (Mathematics) KW - Optimization techniques KW - Optimization theory KW - Systems optimization KW - Mathematical analysis KW - Maxima and minima KW - Operations research KW - Simulation methods KW - System analysis 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 - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Mathematical optimization KW - Artificial Intelligence. UR - https://www.unicat.be/uniCat?func=search&query=sysid:4868902 AB - This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research. ER -