Genetic algorithms basic techniques

Variable selection is an optimization problem. An optimization method that combines randomness with a strategy that is borrowed from biology is a technique using genetic algorithms—a so-called natural computation method (Massart et al. 1997). Actually, the basic structure of GAs is ideal for the purpose of selection (Davis 1991 Hibbert 1993 Leardi 2003), and various applications of GAs for variable selection in chemometrics have been reported (Broadhurst et al. 1997 Jouan-Rimbaud et al. 1995 Leardi 1994, 2001, 2007). Only a brief introduction to GAs is given here, and only from the point of view of variable selection. 

The genetic algorithm, or GA, is perhaps the most popular evolutionary technique amongst musicans. The basic idea behind GAs first appeared in the early 1960s, but John Holland is often quoted as one of the pioneers of GAs for his work on modelling adaptive natural systems (Holland, 1975). 

The cellular automata lookup table synthesis technique is discussed in Chapter 4. LASy basically works by applpng a cellular automaton to a lookup table containing an initial waveform. At each playback cycle of the lookup table, the cellular automaton algorithm processes the waveform. The intention is to let the samples of the lookup table be in perpetual mutation, but according to a sort of genetic code. ... 

---