Multipopulation Evolutionary Strategy HMS
Function that runs gradient method for one deme. Wrapper function for ...
Function that runs one ecr metaepoch. Wrapper function for ecr::ecr.
Euclidean distance
Function that runs one GA metaepoch. Wrapper function for GA::ga.
Factory function for a global stopping condition that stops the comput...
Factory function for a global stopping condition that stops the comput...
Factory function for a global stopping condition that never stops the ...
A S4 class representing a result of hms.
Maximization (or minimization) of a fitness function using Hierarchic ...
Factory function for a local stopping condition that stops a deme afte...
Factory function for a local stopping condition that stops a deme afte...
Factory function for a local stopping condition that stops a deme afte...
Factory function for a trivial local stopping condition that lets a de...
Manhattan distance
A S4 class representing a snapshot of one metaepoch.
Plot method for "hms" class.
plotActiveDemes method for "hms" class.
plotActiveDemes method for "hms" class.
plotPopulation method for "hms" class.
plotPopulation method for "hms" class.
Print method for class "hms".
printBlockedSprouts method for "hms" class.
printBlockedSprouts method for "hms" class.
printTree method for class "hms".
printTree method for class "hms".
Factory function that creates normal mutation function
saveMetaepochsPopulations
saveMetaepochsPopulations method for "hms" class.
Default sprouting condition based on given metric.
Show method for class "hms".
Summary method for class "hms".
The HMS (Hierarchic Memetic Strategy) is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. The HMS makes use of a dynamically-evolving data structure that provides an organization among the component populations. It is a tree with a fixed maximal height and variable internal node degree. Each component population is governed by a particular evolutionary engine. This package provides a simple R implementation with examples of using different genetic algorithms as the population engines. References: J. Sawicki, M. Łoś, M. Smołka, J. Alvarez-Aramberri (2022) <doi:10.1007/s11047-020-09836-w>.