Appendix: Evolutionary Algorithms Parameters

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Selection Methods

In the following table is shown the selection methods that you can be chosen to configurie the evolutionary algorithms skeletons (except CHC).

Selection Methods
Id.
Default Parameter
Name
Parameter Description
0
-
Random
-
1
1
Tournament
Size of sample
2
-
Roulette_Wheel
-
3
100
Ranking
Percent of population that the method uses.
4
0
Best (Rank-based)
The position of an individual in the ordered population
5
0
Worst (Rank-based)
The position of an individual in the reverse-ordered population

These methods can be used in selection section of the cofiguration file and in the configuration of the migration operator.

In the CHC skeleton, the selection methods are not optional. In the selection section of the CHC configuration file, you must choose the parameter (percent of population that will restarted) of replace method and the diverge operator that will used to restart the population whenever convergence is detected.

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Intra-population Operators

Intra-population operators:

Id.
Name
Parameters*
Others
0
Crossover
Application probability
HUX in CHC
1
Mutation
Application probability
bit-flip in CHC
2
Hybrid operator
Application probability
only in newGASA
3-
Other operators
Application probability
-
*Perhaps, the concrete operator implemented by the user needs more parameters

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Inter-population Operators

Intra-population operators are the operators that are applied between sub-populations. Currently, the skeletons only have got a single inter-population operator, the migration operator. In order to configure this operator, you must define:

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