abm.ga: Genetic Algorithm metaheuristic

In evoper: Evolutionary Parameter Estimation for 'Repast Simphony' Models

Genetic Algorithm metaheuristic

Description

An implementation of Genetic Algorithm metaheuristic for parameter estimation

Usage

abm.ga(objective, options = NULL)

Arguments

objective	An instance of ObjectiveFunction (or subclass) class ObjectiveFunction
options	An apropiate instance from a sublclass of Options class

References

[1] John Henry Holland (1992). "Adaptation in Natural and Artificial Systems; An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence". MIT Press, Cambridge, MA, USA. ISBN 0262082136. [2] Zbigniew Michalewiczx (1994). "Genetic Algorithms + Data Structures = Evolution Programs (2nd Ed.)". Springer-Verlag, Berlin, Heidelberg. ISBN 3540580905.

Examples

## Not run: 
 f<- PlainFunction$new(f0.rosenbrock2)

 f$Parameter(name="x1",min=-100,max=100)
 f$Parameter(name="x2",min=-100,max=100)

 or

 f$Parameter0(name="x1",levels=c(0:4))
 f$Parameter0(name="x2",levels=c(-2,-1,0,1,2))

 extremize("tabu", f)

## End(Not run)

evoper documentation built on Jan. 28, 2026, 5:06 p.m.