Genetic Algorithm

GeneticAlgorithm(
    local_search::AbstractAlgorithm
    verbose::Bool = DEFAULT_VERBOSE
    max_iterations::Integer = 200
    max_iterations_without_improvement::Integer = 150
    π_min::Integer = 40
    π_max::Integer = 50
)

GeneticAlgorithm represents a clustering algorithm that utilizes a genetic algorithm approach to optimize cluster assignments. It combines evolutionary computation and local search elements to find high-quality clustering solutions.

Fields

• local_search: the clustering algorithm applied to improve the solution in each meta-heuristics iteration.
• verbose: controls whether the algorithm should display additional information during execution.
• max_iterations: represents the maximum number of iterations the algorithm will perform before stopping.
• max_iterations_without_improvement: represents the maximum number of iterations allowed without improving the best solution.
• π_max: the maximum population size used in the genetic algorithm.
• π_min: the minimum population size used in the genetic algorithm.

References

fit(
    meta::GeneticAlgorithm,
    data::AbstractMatrix{<:Real},
    k::Integer
)

The fit function applies a genetic algorithm to a clustering problem and returns a result object representing the clustering outcome.

Parameters:

• meta: an instance representing the clustering settings and parameters.
• data: a floating-point matrix, where each row represents a data point, and each column represents a feature.
• k: an integer representing the number of clusters.

Example

n = 100
d = 2
k = 2

data = rand(n, d)

kmeans = Kmeans()
genetic_algorithm = GeneticAlgorithm(local_search = kmeans)
result = fit(genetic_algorithm, data, k)