SEARCH_CULL

SEARCH_CULL defines a portion of the population that will be dropped every time partial convergence is reached in the search. The given fraction of the population (or number of members) with the worst \(R\) factors will be removed, and replaced with new configurations generated from the surviving population, or at random. The mode of generating the new configurations can also be defined.

See Kottcke and Heinz [9] for an explanation of the search algorithm used by TensErLEED.

Default: 0.1 genetic

Allowed values: Positive real (0, 1) (percentage of population) OR positive integer (absolute number to be removed); optional: flag clone / genetic / random to define how to create new configurations

Syntax:

SEARCH_CULL = 0.2 genetic   ! cull the worst-performing 20% of the population, replace by offspring from two random survivors
SEARCH_CULL = 0.2           ! equivalent to the above, genetic is default
SEARCH_CULL = 0.2 clone     ! cull the worst-performing 20% of the population, replace by clones of survivors (picked at random)
SEARCH_CULL = 3             ! cull exactly three worst-performing configurations
SEARCH_CULL = 2 random      ! cull exactly two worst-performing configurations, replace them with new configurations re-initialized at random.

For most systems, part of the search population will get stuck in local minima. Culling some of those is generally safe, and may improve performance by increasing the population density in better-performing parts of parameter space.

Defining SEARCH_CULL via a percentage of the overall population is recommended, as this will be more robust to later changes to the population size.

Note

If the offsprings are generated as clones, this automatically improves the average \(R\) factor of the population when restarting the search. If the offsprings are re-initialized as random configurations or via the genetic algorithm, the average \(R\) factor may actually get worse. In either case, you will see a jump of the \(R\) factor in Search-report.pdf/ Search-progress.pdf.

For the genetic mode, each new configuration is generated as follows: Two parents are picked at random from the surviving population (such that the parents are not identical, if possible). Then, for each independent parameter, the new configuration inherits the value from one of the parents at random. Dependent parameters are then filled up in keeping with the constraints of the system (e.g. symmetry). Note that this does not exclude the possibility of the offspring being identical to one of the parents, especially if the parents are similar to begin with.