#Genetic Algorithms Template for genetic algorithms using a population of chromosomes, with related evolutionary behaviors (e.g. generation, selection, crossover, mutation, elitism).
##Basic Definition A genetic algorithm is a specific type of evolutionary algorithm, and it is about searching the solution of a problem using concepts of natural selection and evolution, starting from an initial set of randomly generated hypothesis. Such set is most commonly referred as population.
A population is composed of a list of chromosomes of generic type (representation of the genes). A chromosome is an entity characterized by a fitness score and a series of genes. Each chromosome encodes a possible solution for the addressed problem.
At each step of the algorithm a population is evolved via a set of different operations. First we select a couple of individuals (selection), then we combine them in order to generate new chromosomes (crossover) inserting possible random modifications (mutation). The old populations is then replaced by the newly generated offspring.
An additional technique is called elitism, and is about keeping the current best individuals in the new population.
##Usage A new population can be instantiated and run by providing the genetic parameters and functions.
Genetic parameters are size, crossover-rate and elite-size.
There are three genetic functions required: one for the generation of a new chromosomes, one for the fitness computation and one for the gene mutation.
Released under version 2.0 of the Apache License.