Genetic Algorithm

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Initial Population

Each individual is a possible solution to a problem you want to solve, the individuals are characterised by a set of parameters, that are called genes, these genes ( generally defined by a bit ) are merged into a string to form a Chromosome, that is the solution to the problem

[ 1 0 1 0 (1) -> { Gene } 1 1 0 1 ] -> Chromosome

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Fitness Function

Determines how fit the individual is, giving it a fitness score that determines the probability for an individual to be chosen for reproduction

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Selection

Select the fittest individuals and let them pass their genes to the next generation, the individuals are selected based on their fitness score

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Crossover

It chooses a random point, called the crossover point in the chromosome

[ 0 0 0 1 | -> (Crossover Point) 1 1 ] -> Parent A

[ 1 0 1 1 | 0 0 ] -> Parent B

The parents will exchange their genes until the crossover point is reached

[ 1 0 1 1 | 1 1 ] -> Child A

[ 0 0 0 1 | 0 0 ] -> Child B

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Mutation

Some children may have mutations ( with a low probability ) and some of the bits can be flipped, it is done to maintain the diversity in the population, preventing bias and premature convergence