Fuzzy Evolutionary Cellular Automata

Genetic algorithms are powerful tools that allow engineers and scientists to find good solutions to hard computational problems using evolutionary principles. The classic genetic algorithm has several disadvantages, however. Foremost among these drawbacks is the difficulty of choosing optimal parameter settings. Genetic algorithm literature is full of empirical tricks, techniques, and rules of thumb that enable GAs to be optimized to perform better in some way by altering the GA parameters. Capturing these rules of thumb in fuzzy logic systems and using such systems to dynamically control the parameters of GAs has enabled researchers to create GA systems that outperform conventional GAs. This chapter is a survey of basic parameter adaptation and different ways researchers have integrated fuzzy logic systems into GAs.