The operator module defines the types of genetic operators as traits.
A genetic operator defines a function that performs a specific stage in
the genetic algorithm. Each of these genetic operator can be implemented
in variety of ways using different algorithms and methods.
The genetic operators are the building blocks of the genetic algorithm.
Their different implementations can be combined in a variety of ways to
make up the actual simulation of a specific problem.
| CrossoverOp | A CrossoverOp defines a function of how to crossover two
genetic::Genotypes, often called parent genotypes, to derive new
genetic::Genotypes. It is analogous to reproduction and biological
crossover. Cross over is a process of taking two parent solutions and
producing an offspring solution from them.
|
| GeneticOperator | A GeneticOperator defines a function used to guide the genetic algorithm
towards a solution to a given problem. There are three main types of
operators - Selection, Crossover and Mutation - which must work in
conjunction with one another in order for the algorithm to be successful.
|
| MultiObjective | Marker trait for genetic operators and functions that are used for
multi-objective optimization.
|
| MutationOp | A MutationOp defines a function of how a genetic::Genotype mutates. It
is used to maintain genetic diversity from one generation of a population
of genetic algorithm genotypes to the next. It is analogous to biological
mutation. Mutation alters one or more gene values in a chromosome from its
initial state. In mutation, the solution may change entirely from the
previous solution. Hence GA can come to a better solution by using
mutation. Mutation occurs during evolution according to a user-definable
mutation probability. This probability should be set low. If it is set too
high, the search will turn into a primitive random search.
|
| ReinsertionOp | A ReinsertionOp defines a function that combines the offspring with the
current population to create the population for the next generation.
At the end the new population must be of the same size as the original
population.
|
| SelectionOp | A SelectionOp defines the function of how to select solutions for being
the parents of the next generation.
|
| SingleObjective | Marker trait for genetic operators and functions that are used for
single-objective optimization.
|