Tournament Selection

Tournament selection is used to select the agents from a population which will make up the next generation of agents. If elitism is used, the best agent from a population is automatically preserved and becomes a member of the next generation. Then, for each tournament, k individuals are randomly chosen, and the agent with the best evaluation fitness is preserved. This is repeated until the population for the next generation is full.

The class TournamentSelection() defines the functions required for tournament selection. TournamentSelection.select() returns the best agent, and the new generation of agents.

from agilerl.hpo.tournament import TournamentSelection

tournament = TournamentSelection(tournament_size=2, # Tournament selection size
                                  elitism=True,      # Elitism in tournament selection
                                  population_size=6, # Population size
                                  evo_step=1)        # Evaluate using last N fitness scores

Parameters

class agilerl.hpo.tournament.TournamentSelection(tournament_size, elitism, population_size, eval_loop)

The tournament selection class.

Parameters:

  • tournament_size (int) – Tournament selection size

  • elitism (bool) – Elitism in tournament selection

  • population_size (int) – Number of agents in population

  • eval_loop (int) – Number of most recent fitness scores to use in evaluation

select(population)

Returns best agent and new population of agents following tournament selection.

Parameters:

population (list[object]) – Population of agents