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Optimal subset selection is an important task that has numerous algorithmsdesigned for it and has many application areas. STPGA contains a specialgenetic algorithm supplemented with a tabu memory property (that keeps track ofpreviously tried solutions and their fitness for a number of iterations), andwith a regression of the fitness of the solutions on their coding that is usedto form the ideal estimated solution (look ahead property) to search forsolutions of generic optimal subset selection problems. I have initiallydeveloped the programs for the specific problem of selecting trainingpopulations for genomic prediction or association problems, therefore I givediscussion of the theory behind optimal design of experiments to explain thedefault optimization criteria in STPGA, and illustrate the use of the programsin 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 observationidentification for regression, solving mixed integer quadratic optimizationproblems, balancing gains and inbreeding in a breeding population. Some ofthese illustrations pertain new statistical approaches.

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)