Kwalitee Issues


Add 'use strict' (or its equivalents) to all modules, or convince us that your favorite module is well-known enough and people can easily see the modules are strictly written.

Error: Algorithm::Evolutionary


Split the distribution, or fix the version numbers to make them consistent (use the highest version number to avoid version downgrade).

Error: 0.80,1.005,1.102,1.2,1.3,1.4,1.5,1.7,3.0,3.000,3.001,3.002,3.007,3.1,3.104,3.106,3.2,3.3,3.4,3.5,3.6,3.7


If you are using Build.PL define the {requires}{perl} = VERSION field. If you are using MakeMaker (Makefile.PL) you should upgrade ExtUtils::MakeMaker to 6.48 and use MIN_PERL_VERSION parameter. Perl::MinimumVersion can help you determine which version of Perl your module needs.


Add 'use warnings' (or its equivalents) to all modules (this will require perl > 5.6), or convince us that your favorite module is well-known enough and people can easily see the modules warn when something bad happens.

Error: Algorithm::Evolutionary


Add all modules contained in this distribution to the META.yml field 'provides'. Module::Build or Dist::Zilla::Plugin::MetaProvides do this automatically for you.


Add a 'repository' resource to the META.yml via 'meta_add' accessor (for Module::Build) or META_ADD parameter (for ExtUtils::MakeMaker).


Name Abstract Version View
Algorithm::Evolutionary Perl module for performing paradigm-free evolutionary algorithms. 0.80 metacpan
Algorithm::Evolutionary::Experiment Class for setting up an experiment with algorithms and population 3.4 metacpan
Algorithm::Evolutionary::Fitness::Any Fa├žade for any function so that it can be used as fitness 3.2 metacpan
Algorithm::Evolutionary::Fitness::Base Base class for fitness functions 3.1 metacpan
Algorithm::Evolutionary::Fitness::ECC Error Correcting codes problem generator 3.2 metacpan
Algorithm::Evolutionary::Fitness::Knapsack Fitness function for the knapsack problem 3.1 metacpan
Algorithm::Evolutionary::Fitness::MMDP Massively Multimodal Deceptive Problem 3.0 metacpan
Algorithm::Evolutionary::Fitness::ONEMAX Fitness function for the ONEMAX or count-ones problem 3.0 metacpan
Algorithm::Evolutionary::Fitness::P_Peaks P Peaks problem generator 3.4 metacpan
Algorithm::Evolutionary::Fitness::Rastrigin Implementation of Rastrigin's function 3.3 metacpan
Algorithm::Evolutionary::Fitness::Royal_Road Mitchell's Royal Road function 3.1 metacpan
Algorithm::Evolutionary::Fitness::String Base class for string-based fitness functors 3.0 metacpan
Algorithm::Evolutionary::Fitness::Trap 'Trap' fitness function for evolutionary algorithms 3.2 metacpan
Algorithm::Evolutionary::Fitness::ZDT1 Zitzler-Deb-Thiele #1 Multiobjective test function 3.001 metacpan
Algorithm::Evolutionary::Fitness::wP_Peaks wP Peaks problem generator - weighted version of P_Peaks 3.002 metacpan
Algorithm::Evolutionary::Hash_Wheel Random selector of things depending on probabilities 1.2 metacpan
Algorithm::Evolutionary::Individual::Any Wrapper around any Perl class, turns it into a I<Chromosome> 3.0 metacpan
Algorithm::Evolutionary::Individual::Base Base class for chromosomes that knows how to build them, and has some helper methods. 3.3 metacpan
Algorithm::Evolutionary::Individual::BitString Classic bitstring individual for evolutionary computation; usually called I<chromosome> 3.4 metacpan
Algorithm::Evolutionary::Individual::Bit_Vector Classic bitstring individual for evolutionary computation; usually called chromosome, and using a different implementation from Algorithm::Evolutionary::Individual::BitString 3.1 metacpan
Algorithm::Evolutionary::Individual::String A character string to be evolved. Useful mainly in word games 3.7 metacpan
Algorithm::Evolutionary::Individual::Tree 3.1 metacpan
Algorithm::Evolutionary::Individual::Vector Array as an individual for evolutionary computation 3.2 metacpan
Algorithm::Evolutionary::Op::Animated_GIF_Output Creates an animated GIF, a frame per generation. Useful for binary strings. 1.005 metacpan
Algorithm::Evolutionary::Op::ArithCrossover Arithmetic crossover operator; performs the average of the n parents crossed 3.001 metacpan
Algorithm::Evolutionary::Op::Base Base class for Algorithm::Evolutionary operators, 3.3 metacpan
Algorithm::Evolutionary::Op::Bitflip Bit-flip mutation 3.3 metacpan
Algorithm::Evolutionary::Op::Breeder Even more customizable single generation for an evolutionary algorithm. 1.4 metacpan
Algorithm::Evolutionary::Op::Breeder_Diverser Like Breeder, only it tries to cross only individuals that are different 1.7 metacpan
Algorithm::Evolutionary::Op::CX 3.2 metacpan
Algorithm::Evolutionary::Op::CanonicalGA Canonical Genetic Algorithm, with any representation 3.6 metacpan
Algorithm::Evolutionary::Op::Canonical_GA_NN Canonical Genetic Algorithm that does not rank population 3.6 metacpan
Algorithm::Evolutionary::Op::ChangeLengthMutation Increases/decreases by one atom the length of the string 3.1 metacpan
Algorithm::Evolutionary::Op::Combined Combinator of several operators of the same arity, unary or binary 1.2 metacpan
Algorithm::Evolutionary::Op::Convergence_Terminator Checks for termination of an algorithm, returns true if a certain percentage of the population is the same 3.1 metacpan
Algorithm::Evolutionary::Op::Creator Operator that generates groups of individuals, of the intended class 3.1 metacpan
Algorithm::Evolutionary::Op::Crossover n-point crossover operator; puts fragments of the second operand into the first operand 3.002 metacpan
Algorithm::Evolutionary::Op::DeltaTerm Termination condition for an algorithm; checks that the difference of the best to a target is less than a delta 3.0 metacpan
Algorithm::Evolutionary::Op::EDA_step Single step for a Estimation of Distribution Algorithm 1.5 metacpan
Algorithm::Evolutionary::Op::Easy evolutionary algorithm, single generation, with variable operators. 3.5 metacpan
Algorithm::Evolutionary::Op::Easy_MO Multiobjecttive evolutionary algorithm, single generation, with variable operators 3.6 metacpan
Algorithm::Evolutionary::Op::Eval::General General and simple population evaluator 3.000 metacpan
Algorithm::Evolutionary::Op::Eval::MO_Rank Multiobjective evaluator based on Pareto rank 3.002 metacpan
Algorithm::Evolutionary::Op::FullAlgorithm Skeleton class for a fully-featured evolutionary algorithm 3.0 metacpan
Algorithm::Evolutionary::Op::GaussianMutation Changes numeric chromosome components following the gaussian distribution. 3.4 metacpan
Algorithm::Evolutionary::Op::Gene_Boundary_Crossover n-point crossover operator that restricts crossing point to gene boundaries 3.002 metacpan
Algorithm::Evolutionary::Op::GeneralGeneration Customizable single generation for an evolutionary algorithm. 3.2 metacpan
Algorithm::Evolutionary::Op::Generation_Skeleton Even more customizable single generation for an evolutionary algorithm. 3.3 metacpan
Algorithm::Evolutionary::Op::GenerationalTerm Checks for termination of an algorithm. 3.0 metacpan
Algorithm::Evolutionary::Op::IncMutation Increments/decrements by one the value of one of the components of the string, takes into account the char class 3.001 metacpan
Algorithm::Evolutionary::Op::Inverover Michalewicz's inver-over Operator. 3.0 metacpan
Algorithm::Evolutionary::Op::LinearFreezer Used by Simulated Annealing algorithms, reduces temperature lineally. 3.1 metacpan
Algorithm::Evolutionary::Op::Mutation BitFlip mutation, changes several bits in a bitstring, depending on the probability 3.1 metacpan
Algorithm::Evolutionary::Op::NoChangeTerm Checks for termination of an algorithm; terminates when several generations transcur without change 3.0 metacpan
Algorithm::Evolutionary::Op::Novelty_Mutation Mutation guaranteeing new individual is not in the population 3.001 metacpan
Algorithm::Evolutionary::Op::Permutation Per-mutation. Got it? 3.7 metacpan
Algorithm::Evolutionary::Op::Population_Output Flexible population printing class 3.001 metacpan
Algorithm::Evolutionary::Op::QuadXOver N-point crossover operator that changes operands 3.104 metacpan
Algorithm::Evolutionary::Op::Quad_Crossover_Diff Uniform crossover, but interchanges only those atoms that are different 1.102 metacpan
Algorithm::Evolutionary::Op::Replace_Different Incorporate individuals into the population replacing the worst ones but only if they are different. 1.3 metacpan
Algorithm::Evolutionary::Op::Replace_Worst Incorporate individuals into the population replacing the worst ones 3.2 metacpan
Algorithm::Evolutionary::Op::RouletteWheel Fitness-proportional selection, using a roulette wheel. 3.1 metacpan
Algorithm::Evolutionary::Op::Selector Abstract base class for population selectors 3.0 metacpan
Algorithm::Evolutionary::Op::SimulatedAnnealing 3.1 metacpan
Algorithm::Evolutionary::Op::Storing Applies the op and keeps the result 3.001 metacpan
Algorithm::Evolutionary::Op::StringRand randomly change chars in a string 3.001 metacpan
Algorithm::Evolutionary::Op::String_Mutation Single character string mutation 3.007 metacpan
Algorithm::Evolutionary::Op::Tournament_Selection Tournament selector, takes individuals from one population and puts them into another 1.5 metacpan
Algorithm::Evolutionary::Op::TreeMutation GP-like mutation operator for trees 3.1 metacpan
Algorithm::Evolutionary::Op::Uniform_Crossover interchanges a set of atoms from one parent to the other. 3.2 metacpan
Algorithm::Evolutionary::Op::Uniform_Crossover_Diff Uniform crossover, but interchanges only those atoms that are different 3.106 metacpan
Algorithm::Evolutionary::Op::VectorCrossover Crossover for L<Algorithm::Evolutionary::Individual::Vector>. 3.1 metacpan
Algorithm::Evolutionary::Run Class for setting up an experiment with algorithms and population 3.2 metacpan
Algorithm::Evolutionary::Utils Container module with a hodgepodge of functions 3.4 metacpan
Algorithm::Evolutionary::Wheel Random selector of things depending on probabilities 3.6 metacpan

Other Files

Changes metacpan
MANIFEST metacpan
META.json metacpan
META.yml metacpan
Makefile.PL metacpan
README metacpan