# NAME

Algorithm::Evolutionary::Op::Easy - evolutionary algorithm, single generation, with variable operators.

# SYNOPSIS

``````  my \$easy_EA = new Algorithm::Evolutionary::Op::Easy \$fitness_func;

for ( my \$i = 0; \$i < \$max_generations; \$i++ ) {
print "<", "="x 20, "Generation \$i", "="x 20, ">\n";
\$easy_EA->apply(\@pop );
for ( @pop ) {
print \$_->asString, "\n";
}
}

#Define a default algorithm with predefined evaluation function,
#Mutation and crossover. Default selection rate is 0.4
my \$algo = new Algorithm::Evolutionary::Op::Easy( \$eval );

#Define an easy single-generation algorithm with predefined mutation and crossover
my \$m = new Algorithm::Evolutionary::Op::Bitflip; #Changes a single bit
my \$c = new Algorithm::Evolutionary::Op::Crossover; #Classical 2-point crossover
my \$generation = new Algorithm::Evolutionary::Op::Easy( \$rr, 0.2, [\$m, \$c] );``````

# Base Class

Algorithm::Evolutionary::Op::Base

# DESCRIPTION

"Easy" to use, single generation of an evolutionary algorithm. Takes an arrayref of operators as input, or defines bitflip-mutation and 2-point crossover as default. The `apply` method applies a single iteration of the algorithm to the population it takes as input

# METHODS

## new( \$eval_func, [\$operators_arrayref] )

Creates an algorithm that optimizes the handled fitness function and reference to an array of operators. If this reference is null, an array consisting of bitflip mutation and 2 point crossover is generated. Which, of course, might not what you need in case you don't have a binary chromosome.

## set( \$hashref, codehash, opshash )

Sets the instance variables. Takes a ref-to-hash (for options), codehash (for fitness) and opshash (for operators)

## apply( \$population )

Applies the algorithm to the population; checks that it receives a ref-to-array as input, croaks if it does not. Returns a sorted, culled, evaluated population for next generation.