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Manual Reference Pages  -  AI::GENETIC (3)

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AI::Genetic - A pure Perl genetic algorithm implementation.



    use AI::Genetic;
    my $ga = new AI::Genetic(
        -fitness    => \&fitnessFunc,
        -type       => bitvector,
        -population => 500,
        -crossover  => 0.9,
        -mutation   => 0.01,
        -terminate  => \&terminateFunc,

     $ga->evolve(rouletteTwoPoint, 100);
     print "Best score = ", $ga->getFittest->score, ".\n";

     sub fitnessFunc {
         my $genes = shift;

         my $fitness;
         # assign a number to $fitness based on the @$genes
         # ...

         return $fitness;

      sub terminateFunc {
         my $ga = shift;

         # terminate if reached some threshold.
         return 1 if $ga->getFittest->score > $THRESHOLD;
         return 0;


This module implements a Genetic Algorithm (GA) in pure Perl. Other Perl modules that achieve the same thing (perhaps better, perhaps worse) do exist. Please check CPAN. I mainly wrote this module to satisfy my own needs, and to learn something about GAs along the way.

<B>PLEASE NOTE:B> As of v0.02, AI::Genetic has been re-written from scratch to be more modular and expandable. To achieve this, I had to modify the API, so it is not backward-compatible with v0.01. As a result, I do not plan on supporting v0.01.

I will not go into the details of GAs here, but here are the bare basics. Plenty of information can be found on the web.

In a GA, a population of individuals compete for survival. Each individual is designated by a set of genes that define its behaviour. Individuals that perform better (as defined by the fitness function) have a higher chance of mating with other individuals. When two individuals mate, they swap some of their genes, resulting in an individual that has properties from both of its parents. Every now and then, a mutation occurs where some gene randomly changes value, resulting in a different individual. If all is well defined, after a few generations, the population should converge on a good-enough solution to the problem being tackled.

A GA implementation runs for a discrete number of time steps called generations. What happens during each generation can vary greatly depending on the strategy being used (See STRATEGIES for more info). Typically, a variation of the following happens at each generation:
<B>1. SelectionB> Here the performance of all the individuals is evaluated based on the fitness function, and each is given a specific fitness value. The higher the value, the bigger the chance of an individual passing its genes on in future generations through mating (crossover).
<B>2. CrossoverB> Here, individuals selected are randomly paired up for crossover (aka sexual reproduction). This is further controlled by the crossover rate specified and may result in a new offspring individual that contains genes common to both parents. New individuals are injected into the current population.
<B>3. MutationB> In this step, each individual is given the chance to mutate based on the mutation probability specified. If an individual is to mutate, each of its genes is given the chance to randomly switch its value to some other state.


Here are the public methods.
$ga-><B>newB>(options) This is the constructor. It accepts options in the form of hash-value pairs. These are:
<B>-populationB> This defines the size of the population, i.e. how many individuals to simultaneously exist at each generation. Defaults to 100.
<B>-crossoverB> This defines the crossover rate. Defaults to 0.95.
<B>-mutationB> This defines the mutation rate. Defaults to 0.05.
-fitness This defines a fitness function. It expects a reference to a subroutine. More details are given in FITNESS FUNCTION.
-type This defines the type of the genome. Currently, AI::Genetic supports only three types:
bitvector Individuals of this type have genes that are bits. Each gene can be in one of two possible states, on or off.
listvector Each gene of a listvector individual can assume one string value from a specified list of possible string values.
rangevector Each gene of a rangevector individual can assume one integer value from a range of possible integer values. Note that only integers are supported. The user can always transform any desired fractional values by multiplying and dividing by an appropriate power of 10.

Defaults to bitvector.

-terminate This option allows the definition of a termination subroutine. It expects a subroutine reference. This sub will be called at the end of each generation with one argument: the AI::Genetic object. Evolution terminates if the sub returns a true value.
$ga-><B>createStrategyB>(strategy_name, sub_ref) This method allows the creation of a custom-made strategy to be used during evolution. It expects a unique strategy name, and a subroutine reference as arguments. The subroutine will be called with one argument: the AI::Genetic object. It is expected to alter the population at each generation. See STRATEGIES for more information.
$ga-><B>initB>(initArgs) This method initializes the population with random individuals. It <B>MUSTB> be called before any call to evolve() or inject(). As a side effect, any already existing individuals in the population are deleted. It expects one argument, which depends on the type of individuals:
o For bitvectors, the argument is simply the length of the bitvector.


this initializes a population where each individual has 10 genes.

o For listvectors, the argument is an anonymous list of lists. The number of sub-lists is equal to the number of genes of each individual. Each sub-list defines the possible string values that the corresponding gene can assume.

               [qw/red blue green/],
               [qw/big medium small/],
               [qw/very_fat fat fit thin very_thin/],

this initializes a population where each individual has 3 genes, and each gene can assume one of the given values.

o For rangevectors, the argument is an anonymous list of lists. The number of sub-lists is equal to the number of genes of each individual. Each sub-list defines the minimum and maximum integer values that the corresponding gene can assume.

               [1, 5],
               [0, 20],
               [4, 9],

this initializes a population where each individual has 3 genes, and each gene can assume an integer within the corresponding range.

$ga-><B>injectB>(N, ?args?) This method can be used to add more individuals to the population. New individuals can be randomly generated, or be explicitly specified. The first argument specifies the number, N, of new individuals to add. This can be followed by at most N arguments, each of which is an anonymous list that specifies the genome of a single individual to add. If the number of genomes given, n, is less than N, then N - n random individuals are added for a total of N new individuals. Random individuals are generated using the same arguments passed to the init() method. For example:

              [qw/red big thin/],
              [qw/blue small fat/],

this adds 5 new individuals, 2 with the specified genetic coding, and 3 randomly generated.

$ga-><B>evolveB>(strategy, ?num_generations?) This method causes the GA to evolve the population using the specified strategy. A strategy name has to be specified as the first argument. The second argument is optional and specifies the number of generations to evolve. It defaults to 1. See STRATEGIES for more information on the default strategies.

Each generation consists of the following steps:
o The population is sorted according to the individuals’ fitnesses.
o The subroutine corresponding to the named strategy is called with one argument, the AI::Genetic object. This subroutine is expected to alter the object itself.
o If a termination subroutine is given, it is executed and the return value is checked. Evolution terminates if this sub returns a true value.

$ga-><B>getFittestB>(?N?) This returns the N fittest individuals. If not specified, N defaults to 1. As a side effect, it sorts the population by fitness score. The actual AI::Genetic::Individual objects are returned. You can use the genes() and score() methods to get the genes and the scores of the individuals. Please check AI::Genetic::Individual for details.
$ga-><B>sortPopulationB> This method sorts the population according to fitness function. The results are cached for speed.
$ga-><B>sortIndividualsB>(?[ListOfIndividuals]?) Given an anonymous list of individuals, this method sorts them according to fitness, returning an anonymous list of the sorted individuals.
$ga-><B>peopleB>() Returns an anonymous list of individuals of the current population. <B>IMPORTANTB>: the actual array reference used by the AI::Genetic object is returned, so any changes to it will be reflected in $ga.
$ga-><B>sizeB>(?newSize?) This method is used to query and set the population size.
$ga-><B>crossProbB>(?newProb?) This method is used to query and set the crossover rate.
$ga-><B>mutProbB>(?newProb?) This method is used to query and set the mutation rate.
$ga-><B>indTypeB>() This method returns the type of individual: bitvector, listvector, or rangevector.
$ga-><B>generationB>() This method returns the current generation.


Very quickly you will realize that properly defining the fitness function is the most important aspect of a GA. Most of the time that a genetic algorithm takes to run is spent in running the fitness function for each separate individual to get its fitness. AI::Genetic tries to minimize this time by caching the fitness result for each individual. But, <B>you should spend a lot of time optimizing your fitness function to achieve decent run times.B>

The fitness function should expect only one argument, an anonymous list of genes, corresponding to the individual being analyzed. It is expected to return a number which defines the fitness score of the said individual. The higher the score, the more fit the individual, the more the chance it has to be chosen for crossover.


AI::Genetic comes with 9 predefined strategies. These are:
rouletteSinglePoint This strategy implements roulette-wheel selection and single-point crossover.
rouletteTwoPoint This strategy implements roulette-wheel selection and two-point crossover.
rouletteUniform This strategy implements roulette-wheel selection and uniform crossover.
tournamentSinglePoint This strategy implements tournament selection and single-point crossover.
tournamentTwoPoint This strategy implements tournament selection and two-point crossover.
tournamentUniform This strategy implements tournament selection and uniform crossover.
randomSinglePoint This strategy implements random selection and single-point crossover.
randomTwoPoint This strategy implements random selection and two-point crossover.
randomUniform This strategy implements random selection and uniform crossover.
More detail on these strategies and how to call them in your own custom strategies can be found in AI::Genetic::OpSelection, AI::Genetic::OpCrossover and AI::Genetic::OpMutation.

You can use the functions defined in the above modules in your own custom-made strategy. Consult their manpages for more info. A custom-made strategy can be defined using the strategy() method and is called at the beginning of each generation. The only argument to it is the AI::Genetic object itself. Note that the population at this point is sorted accoring to each individual’s fitness score. It is expected that the strategy sub will modify the population stored in the AI::Genetic object. Here’s the pseudo-code of events:

    for (1 .. num_generations) {
      sort population;
      call strategy_sub;
      if (termination_sub exists) {
        call termination_sub;
        last if returned true value;


Genetic algorithms are inherently slow. Perl can be pretty fast, but will never reach the speed of optimized C code (at least my Perl coding will not). I wrote AI::Genetic mainly for my own learning experience, but still tried to optimize it as much as I can while trying to keep it as flexible as possible.

To do that, I resorted to some well-known tricks like passing a reference of a long list instead of the list itself (for example, when calling the fitness function, a reference of the gene list is passed), and caching fitness scores (if you try to evaluate the fitness of the same individual more than once, then the fitness function will not be called, and the cached result is returned).

To help speed up your run times, you should pay special attention to the design of your fitness function since this will be called once for each unique individual in each generation. If you can shave off a few clock cycles here and there, then it will be greatly magnified in the total run time.


I have tested this module quite a bit, and even used it to solve a work-related problem successfully. But, if you think you found a bug then please let me know, and I promise to look at it.

Also, if you have any requests, comments or suggestions, then feel free to email me.


Either the usual:

    perl Makefile.PL
    make install

or just stick it somewhere in @INC where perl can find it. It is in pure Perl.


Written by Ala Qumsieh

Special thanks go to John D. Porter and Oliver Smith for stimulating discussions and great suggestions. Daniel Martin and Ivan Tubert-Brohman uncovered various bugs and for this I’m grateful.


(c) 2003-2005 Ala Qumsieh. All rights reserved. This module is distributed under the same terms as Perl itself.
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