Learning the Genetic Algorithm Lingo
from Genetic Algorithms, by David Goldberg

        Comparisons of Natural and GA Terminology
   
    Natural                       Genetic Algorithm
  ---------------------------------------------------
    chromosome                         string           (such as "0 1 1 0 1")
    gene                 feature, character, or detector  (one of the bits)
    allele                          feature value  (such as: is it a 1 or a 0?) 
    locus                          string position
    genotype                          structure
    phenotype               parameter type, alternative solution,
                                  a decoded solution
    epistasis                        non-linearity


    Rougly speaking, the chromosomes in biological systems are analogous
to the strings of artificial genetic systems. In natural systems, one or
more chromosomes combine to form the total genetic prescription for the 
construction and operation of some organism.  In natural systems the 
total genetic package is called the genotype.  In artificial genetic
systems the total package of strings is called a structure.  At this
introductory level, the structure consists of a single string.  In 
natural systmes, the organism formed by the interaction of the total
genetic package with its environment is called the phenotype.  In 
artificial genetic systems, the structures decode to form a particular
parameter set, solution alternative, or point in the solution space.
     Chromosomes are composed of genes, which may take on some number of
values called alleles.  In genetics, the position of a gene (its locus)
is identified separately from the gene's function.  We can talk of a 
particular gene, for example an animal's eye color gene, its locus,
position 10, and its allele value, blue eyes.  In artificial genetic
search we say that strings are composed of features or detectors, which
take on different values.  Features may be located at different positions
on the string.