ABSTRACT
Parameter design using computational intelligence Through hundreds of years of efforts by scholars, science and technology have progressed rapidly. Problems that were considered unsolvable are now solved; however, until now, many nondeterministic polynomial-time hard (NP-hard) problems are still unsolvable. When traditional methodologies and approaches prove ineffective or infeasible, computational intelligence approaches are frequently employed to solve NP-hard problems by the help of the current fast and strong computing ability of computers. Computational intelligence, primarily including fuzzy logic systems, neural networks (NNs), and evolutionary computations, is a set of nature-inspired computational methodologies and approaches to address complex problems with the real-world applications. This chapter introduces how to employ the computational intelligence approach to solve parameter design problems. Because of the wide scope of computational intelligence, we first briefly introduce NNs and genetic algorithms (GAs) and then describe how to apply these approaches to solve parameter design problems.
