Genetic Algorithms


GAs are search heuristics inspired on the process of natural evolution. In its simplest form, GAs use a population of individuals to model a distribution; each individual's dna code represents a single state in the state space, and a fitness function influences the individuals who will survive and reproduce at each generation. GAs have two way of exploring states, either by Mutation (changing the individual's dna) or by Cross-Over (mixing two individual's dna)  More information on Wikipedia.

In the implementation presented here, the individual's dna is a 64 bit representation of two-dimensional coordiantes. Mutation is a displacement in space, and Cross-Over selects a random index on the 64bit dna and swaps the two parent's chromosomes to generate two new individuals.

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