Title: Selection of training populations (and other subset selection problems) with an accelerated genetic algorithm (STPGA: An R-package for selection of training populations with a genetic algorithm),
Abstract: Optimal subset selection is an important task that has numerous algorithms
designed for it and has many application areas. STPGA contains a special
genetic algorithm supplemented with a tabu memory property (that keeps track of
previously tried solutions and their fitness for a number of iterations), and
with a regression of the fitness of the solutions on their coding that is used
to form the ideal estimated solution (look ahead property) to search for
solutions of generic optimal subset selection problems. I have initially
developed the programs for the specific problem of selecting training
populations for genomic prediction or association problems, therefore I give
discussion of the theory behind optimal design of experiments to explain the
default optimization criteria in STPGA, and illustrate the use of the programs
in this endeavor. Nevertheless, I have picked a few other areas of application:
supervised and unsupervised variable selection based on kernel alignment,
supervised variable selection with design criteria, influential observation
identification for regression, solving mixed integer quadratic optimization
problems, balancing gains and inbreeding in a breeding population. Some of
these illustrations pertain new statistical approaches.