## Documentation

Syntax and semantics of the XML files used in OPEAL

Canonical Genetic Algorithm on a simple fitness function

Implementation of the Tide optimization using A::E

Optimization of the tide function using A::E

## Modules

Perl module for performing paradigm-free evolutionary algorithms.

Class for setting up an experiment with algorithms and population

Façade for any function to look like fitness

Base class for Fitness functions

Error Correcting codes problem generator

Fitness function for the knapsack problem

Massively Multimodal Deceptive Problem

Fitness function for the ONEMAX or count-ones problem

P Peaks problem generator

Mitchell's Royal Road function

Base class for string-based fitness functors

Error Correcting codes problem generator

Zitzler-Deb-Thiele #1 Multiobjective test function

wP Peaks problem generator - weighted version of P_Peaks

Wrapper around any Perl class, turns it into a Chromosome

Base class for chromosomes that knows how to build them, and has some helper methods.

Classic bitstring individual for evolutionary computation; usually called chromosome

Classic bitstring individual for evolutionary computation; usually called chromosome, and using a different implementation from Algorithm::Evolutionary::Individual::BitString

A character string to be evolved. Useful mainly in word games

A Direct Acyclic Graph, or tree, useful for Genetic Programming-Style stuff

Array as an individual for evolutionary computation

Arithmetic crossover operator; performs the average of the n parents crossed

Base class for OPEAL operators; operators are any object with the "apply" method, which does things to individuals or populations.

Bit-flip mutation

Canonical Genetic Algorithm, with any representation

Increases/decreases by one the length of the string

Checks for termination of an algorithm, returns true if a certain percentage of the population is the same

Operator that generates groups of individuals, of the intended class

n-point crossover operator; puts fragments of the second operand into the first operand

Termination condition for an algorithm; checks that the difference of the best to a target is less than a delta

evolutionary algorithm, single generation, with variable operators.

General and simple population evaluator

Multiobjective evaluator based on Pareto rank

Skeleton class for a fully-featured evolutionary algorithm

Changes numeric chromosome components following the gaussian distribution

n-point crossover operator that restricts crossing point to gene boundaries

Customizable single generation for an evolutionary algorithm.

Even more customizable single generation for an evolutionary algorithm.

Checks for termination of an algorithm.

Increments/decrements by one the value of one of the components of the string, takes into account the char class

Michalewicz's inver-over Operator.

used by Simulated Annealing algorithms, reduces temperature lineally.

BitFlip mutation, changes several bits in a bitstring, depending on the probability

Checks for termination of an algorithm; terminates when several generations transcur without change

Mutation guaranteeing new individual is not in the population

Per-mutation. Got it?

Flexible population printing class

n-point crossover operator; puts a part of the second operand into the first operand; can be 1 or 2 points.

Incorporate an individual into the population replacing the worst one

Fitness-proportional selection, using a roulette wheel

Abstract base class for population selectors

An operator that performs the simulated annealing algorithm on an individual, using an external freezing schedule

Applies the op and keeps the result

Tournament selector, takes individuals from one population and puts them into another

GP-like mutation operator for trees

interchanges a set of atoms from one parent to the other.

Crossover for Algorithm::Evolutionary::Individual::Vector.

Class for setting up an experiment with algorithms and population

Container module with a hodgepodge of functions

Random selector of things depending on probabilities