Algorithm::MasterMind::EDA - Solver using an Estimation of Distribution Algorithm
use Algorithm::MasterMind::EDA; my $secret_code = 'EAFC'; my $population_size = 200; my @alphabet = qw( A B C D E F ); my $solver = new Algorithm::MasterMind::EDA { alphabet => \@alphabet, length => length( $secret_code ), pop_size => $population_size}; #The rest, same as the other solvers
Uses Algorithm::Evolutionary instance of EDAs to solve MM; as there are two different fitness functions you can use; probably fitness_orig works better.
fitness_orig
Performs bookkeeping, and assigns flags depending on the initialization values
This function, and all the rest, are directly inherited from base
Yields the first combination
Issues the next combination
Obtain the result to the last combination played
Total number of guesses
Total number of combinations checked to issue result
Returns the number of rules in the algorithm
Returns the rules (combinations, blacks, whites played so far) y a reference to array
Returns a hash with the number of matches, and whether it matches every rule with the number of blacks and whites it obtains with each of them
Computes fitness summing the number of correct black and whites plus the number of rules the combination meets times the length
Fitness proposed in the Applied and Soft Computing paper, difference between the number of blacks/whites obtained by rules against the secret code and by the combination against the combination in the rule.
Other solvers: Algorithm::MasterMind::Sequential and Algorithm::MasterMind::Random. Don't work as well, really.
JJ Merelo <jj@merelo.net>
<jj@merelo.net>
Copyright (c) 2009, JJ Merelo <jj@merelo.net>. All rights reserved.
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To install Algorithm::MasterMind, copy and paste the appropriate command in to your terminal.
cpanm
cpanm Algorithm::MasterMind
CPAN shell
perl -MCPAN -e shell install Algorithm::MasterMind
For more information on module installation, please visit the detailed CPAN module installation guide.