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   _____                      __             _    __      _                  |\n|   |  __ \\                    / /            | |   \\ \\    (_)                 |\n|   | |  \\/ ___ _ __   ___    | |   _ __      | |_   | |    _  ___             |\n|   | | __ / _ \\ '_ \\ / _ \\  / /   | '__|     | __|   \\ \\  | |/ __|            |\n|   | |_\\ \\  __/ | | |  __/  \\ \\   | |     _  | |_    / /  | | (__             |\n|    \\____/\\___|_| |_|\\___|   | |  |_|    ( )  \\__|  | |   |_|\\___|            |\n|                              \\_\\        |/        /_/                        |\n|                                                                              |\n|                                                                              |\n--------------------------------------------------------------------------------\n| Written by Thomas Schaffer \u003cthomas.schaffer@epitech.eu\u003e in Java.             |\n| Licensed under the MIT license.                                              |\n--------------------------------------------------------------------------------\n\n--------------------------------------------------------------------------------\n| Description                                                                  |\n--------------------------------------------------------------------------------\n\nGene{r,t}ic is a somewhat generic framework for genetic algorithms\napplications written in Java.\n\nAll the evolution logic is handled by the main GeneticAlgorithm class and\na few delegate helper classes (Candidate selector, Population generator...).\nHelper classes are abstracted through interfaces and one or several\nimplementations are included for each.\n\nThe implementation completely abstracts away the internal representation\nof candidate solutions AND the genetic operators that operate them,\nallowing for reasonably generic code.\n\nImplementing concrete candidates along with their specialized operators,\nand a suitable fitness evaluation function is the responsability of the user.\nExample implementations of generic candidates (binary form,\nreal numbers set, ...) are included, in which case they only need a fitness\nevaluation function.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Floopfz%2Fgene-r-t-ic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Floopfz%2Fgene-r-t-ic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Floopfz%2Fgene-r-t-ic/lists"}