A clustering algorithm using an evolutionary programming-based approach
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Memetic differential evolution methods for clustering problems
2021, Pattern RecognitionCitation Excerpt :While suitably choosing good starting points may reveal as a good strategy, several other approaches are available in order to improve the exploration capabilities of the overall algorithm in a less problem–specific way, by adapting some general purpose strategies. Over the years, a variety of heuristics and metaheuristics were proposed in order to enhance the solution quality in terms of the MSSC objective: the classical metaheuristic frameworks (simulated annealing, tabu search, variable neighborhood search, iterated local search, evolutionary algorithms [8–13]) were applied, as well as more recent incremental methods and convex optimization techniques [14–17]. None of them were predominantly used in machine learning applications because of restrictions about data size and computational time, and/or limited availability of implementations.
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