Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies
IGI Global Snippet, Jan 1, 2009 - Medical - 605 pages
Today, nature is used as a source of inspiration for the development of new techniques for solving complex problems in various domains, from engineering to biology, with innovative adaptations under investigation. The Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies provides the latest empirical research findings, theoretical frameworks, and technologies of natural computing and artificial immune systems (AIS). An excellent reference source for professionals, researchers, and academicians within the AIS and natural computing fields, this comprehensive collection written by leading international experts proposes new ideas, methods, and theories to solve problems of engineering and science.
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Artificial Immune Systems as a BioInspired Optimization Technique and Its Engineering Applications
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adaptive learning affinity ant colony optimization antibody antigen application approach artificial immune controller artificial immune system Artorg automata automaton behavior binary bio-entities bio-network cells chaotic neural networks chapter China clonal selection clonal selection algorithm clones clustering components convergence detecting dimension distribution dynamic Engineering environment evaluations evolutionary algorithms Evolutionary Computation feature selection Figure function fuzzy chaotic neural genetic algorithm global grid Harbin Engineering University hardware immune system IEEE immune algorithm immune network immune response inductive Turing machine initial input inspired Intelligence interactions International Conference layer memory method multiobjective optimization mutation neuron nodes objective ofthe operator optimization algorithm optimization problem optimum output PAIA2 PAISA parallel computing parameters pheromone population proposed pursuers robust scheduling scheduling scheme selection algorithm simulation solution solve space static web system strategy Tarakanov theory Turing machine University unknown non-selfs variable vector Wang Xidian University