Genetic Programming

Evolutionary.TreeGP — Type

Implementation of Koza-type (tree-based) Genetic Programming

The constructor takes following keyword arguments:

populationSize: The size of the population
terminals: A dictionary of terminals with their their corresponding dimensionality
- This dictionary contains (Terminal, Int) pairs
- The terminals can be any symbols (variables), constat values, or 0-arity functions.
functions: A collection of functions with their corresponding arity.
- This dictionary contains (Function, Int) pairs
initialization: A strategy for population initialization (default: :grow)
- Possible values: :grow and :full
mindepth: Minimal depth of the expression (default: 0)
maxdepth: Maximal depth of the expression (default: 3)
mutation: A mutation function (default: crosstree)
crossover: A crossover function (default: subtree)
simplify: An expression simplification function (default: :nothing)
optimizer: An evolutionary optimizer used for evolving the expressions (default: GA)
- Use mutation and crossover parameters to specify GP-related mutation operation.
- Use selection parameter to specify the offspring selection procedure

Description

The Genetic Programming is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs. It is essentially a heuristic search technique, i.e. searching for an optimal or at least suitable program among the space of all programs^[1].

Auxiliary Functions

Evolutionary.Expression — Type

Julia expression wrapper

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Evolutionary.randterm — Function

randterm(t::TreeGP)

Returns a random terminal given the specification from the TreeGP object t.

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Evolutionary.simplify! — Function

simplify!(expr)

Simplify an algebraic expression

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References

1John R. Koza, "Genetic Programming: On the Programming of Computers by Means of Natural Selection", MIT Press, 1992.