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J. J. Merelo-Guervós > Algorithm-MasterMind-v0.4.5 > Algorithm::MasterMind::EDA_Partitions

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Module Version: 1.003

NAME

Algorithm::MasterMind::EDA_Partitions - Solver using an EDA plus partitions

SYNOPSIS

```    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```

DESCRIPTION

Uses Algorithm::Evolutionary instance of EDAs to solve MM; as there are two different fitness functions you can use; probably `fitness_orig` works better.

INTERFACE

initialize

Performs bookkeeping, and assigns flags depending on the initialization values

new ( \$options )

This function, and all the rest, are directly inherited from base

issue_next()

Issues the next combination

feedback()

Obtain the result to the last combination played

guesses()

Total number of guesses

evaluated()

Total number of combinations checked to issue result

number_of_rules ()

Returns the number of rules in the algorithm

rules()

Returns the rules (combinations, blacks, whites played so far) y a reference to array

matches( \$string )

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

fitness( \$individual )

Computes fitness summing the number of correct black and whites plus the number of rules the combination meets times the length

fitness_orig( \$individual )

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.

AUTHOR

JJ Merelo `<jj@merelo.net>`

Copyright (c) 2009, JJ Merelo `<jj@merelo.net>`. All rights reserved.