Chapter nine - Computational intelligence implementations

Abstract

This chapter discusses the implementation of fuzzy evolutionary fuzzy rule systems. In the system, a genetic algorithm is used to design the fuzzy rule system for solving problems, and another fuzzy rule system is employed to adapt the genetic algorithm. The performance of a genetic algorithm depends on the relationship between exploration and exploitation—the selection of its parameters. The adjustment (adaptation) of a genetic algorithm can occur on four levels: environment, population, individual, and component. In an environment-level adaptation, the environment itself is changed over the course of the search process, and the fitness function, which measures how well an individual fits into the environment, is adapted to reflect the altered environment. Most adaptation is performed by adjusting parameters at the population level; for example, if a particular crossover (mutation) rate is used over the entire population, this crossover (mutation) rate is a candidate to undergo adaptation. In some implementations, each individual has its own mutation rate, so the adaptation of the mutation rate is performed at the individual level.