*Allele* |
Allele,
in biology, is the term given to the appropriate range of values for genes.
In genetic algorithms, an allele is the value of the gene (or genes). |

A*llele
Loss* |
Allele
loss is the natural loss of traits in the gene pool over the generations
of a run. Another term for allele loss is convergence. Severe allele loss
results in a population incapable of solving the problem with the available
gene pool. |

*Atom* |
Atoms
are the mathematical or symbolic operators in the parse tree. Atoms are
always internal (non-leaf) nodes as all atoms accept arguments. |

*Closure* |
Closure
is based on the design of genetic operators to ensure the validity of functions
generated by the genetic program. If closure is achieved, the functions
will not cause errors, regardless of their arrangement, when their fitness
is tested. |

*Convergence* |
Convergence
is a reduction in the diversity of genes available in the population due
to fitness proportionate operators. |

*Diversity* |
Diversity
is the term used to describe the relative uniqueness of each individual
in the population. This condition is considered favourable as the greater
the variety of genes available to the genetic algorithm the greater the
likelihood of the system identifying alternate solutions. |

*Function
Set* |
The
set of functions (atoms) available to the genetic program. The operators
in the function set are used to make up the internal nodes of the parse
tree. |

*Generation* |
A
generation is an iteration of the genetic algorithm. Conventionally, the
initial random generation is known as generation zero. |

*Generational
Equivalent* |
Generational
equivalent is a term used in steady state techniques to identify a generation
has occurred. Because the populations of two generation are combined during
the selection and breeding cycle, a generational equivalent is said to
occur when the number of genetic operations is equal to the population
size of the genetic program. |

*Generational
GP* |
Generational
genetic programming is the process of producing distinct generations in
each iteration of the genetic algorithm. |

*Genotype* |
The
genotype is the structure of the solution produced by the genetic program. |

*Inversion* |
Inversion
is a genetic operator sometimes used in genetic algorithms where two points
in the string are chosen and the order of the genes between those two points
are reversed (inverted). |

*Leaf
Node* |
A
leaf node is a node in a parse tree with no children; a terminal. |

*Non-leaf
Node * |
A
non-leaf node is a node in a parse tree with one or more children; a function. |

*Non-Linear
problems* |
Non-linear
problems are those problems where the relationship between the inputs and
the outputs of the problem are not clearly discernible. |

*NP-complete* |
^{Non-Deterministic
Polynomial Complete is a term used in complexity theory to identify a particular
class of problem. In an NP-complete problem, the relationship between the
number of input parameters to the problem and the problem complexity is
exponential. If an enumerative search strategy is adopted, this exponential
increase in problem complexity results in an exponential increase in the
time to solve the problem.} |

^{Off-line} |
^{ Off-line
learning is a learning method where the systems learning is conducted prior
to its practical application. When the system is actually used, no learning
takes place.} |

^{On-line} |
^{On-line
learning is a learning method where the system learns while it is being
used. This learning method is more robust than off-line learning as the
system can adapt to changes that may occur in the systems environment.} |

^{Parse
Tree} |
^{A
parse tree is the way the genetic programming paradigm represents the functions
generated by a genetic program. A parse tree is similar to a binary decision
tree and preorder traversal of the tree produces an S-expression that represents
a potential solution in reverse polish notation.} |

^{Parsimony} |
^{Parsimony
is the simplicity of the structure of a function.} |

^{Recombination} |
^{Recombination
is another name for crossover.} |

^{Reproduction} |
^{Reproduction
is the copying of a chromosome into the next generation, i.e. budding.} |

^{S-expression} |
^{ S-expressions
are more frequently called LISP expressions and represent a function and
its arguments. S-expressions are the most common representation of functions
generated in genetic programming.} |

^{Structural
Complexity } |
^{Structural
complexity is the term used to describe the number of nodes in the parse
tree, i.e. Number of Terminals + Number of Atoms = Structural Complexity.} |

^{Sufficiency} |
^{Sufficiency
refers to a necessary variety of atoms and terminals available to the genetic
program to solve the problem.} |

^{Terminals} |
^{Terminals
are the numeric values, (variables, constants and zero argument functions)
in the parse tree and are always external (leaf) nodes in the tree. The
terminals act as arguments for the operator (atom) that is their parent
in the tree.} |

^{Terminal
Set} |
^{The
terminal set is comprised of the terminals available to the genetic program.} |

^{Tuning} |
^{Tuning
is the process of selecting the appropriate genetic operators and their
respective parameters to suit a problem.} |

^{Wrapper} |
^{A
wrapper is an interpretative function that evaluates the expression to
be tested and returns a value within a suitable range for the simulation.} |