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J. J. Merelo-Guervós > Algorithm-Evolutionary-0.79_1 > Algorithm::Evolutionary::Fitness::P_Peaks

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Module Version: 3.4   Source   Latest Release: Algorithm-Evolutionary-0.80

# NAME

Algorithm::Evolutionary::Fitness::P_Peaks - P Peaks problem generator

# SYNOPSIS

```    my \$number_of_peaks = 100;
my \$number_of_bits = 32;
my \$p_peaks = Algorithm::Evolutionary::Fitness::P_Peaks->new( \$number_of_peaks, \$number_of_bits );```

# DESCRIPTION

P_Peaks fitness function; optimizes the distance to the closest in a series of peaks. The P-Peaks problem was proposed by Kennedy and Spears in

```  @conference{kennedy1998matching,
title={{Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator}},
author={Kennedy, J. and Spears, W.M.},
booktitle={Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on},
pages={78--83},
isbn={0780348699},
year={1998},
organization={IEEE}
}```

And the optimum is 1.0. By default, result is cached, so be careful when working with long strings and/or big populations

# METHODS

## new( \$peaks, \$bits )

Creates a new instance of the problem, with the said number of bits and peaks.

## random_string()

Returns random string in the same style than the peaks. Useful for testing.

## _really_apply( \$string )

Applies the instantiated problem to a chromosome. Intended for internal use.

## p_peaks( \$string )

Returns the distance to the closest bitstring

# Copyright

```  This file is released under the GPL. See the LICENSE file included in this distribution,
or go to http://www.fsf.org/licenses/gpl.txt```
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