{ "_id": "6a105cbcacfb0bcc41ca44c5", "Package": "adana", "Title": "Adaptive Nature-Inspired Algorithms for Hybrid Genetic\nOptimization", "Version": "1.1.0", "Date": "2022-02-01", "Author": "Zeynel Cebeci [aut, cre], Erkut Tekeli [aut], Cagatay Cebeci\n[aut]", "Maintainer": "Erkut Tekeli ", "Description": "The Genetic Algorithm (GA) is a type of optimization\nmethod of Evolutionary Algorithms. It uses the biologically\ninspired operators such as mutation, crossover, selection and\nreplacement.Because of their global search and robustness\nabilities, GAs have been widely utilized in machine learning,\nexpert systems, data science, engineering, life sciences and\nmany other areas of research and business. However, the regular\nGAs need the techniques to improve their efficiency in\ncomputing time and performance in finding global optimum using\nsome adaptation and hybridization strategies. The adaptive GAs\n(AGA) increase the convergence speed and success of regular GAs\nby setting the parameters crossover and mutation probabilities\ndynamically. The hybrid GAs combine the exploration strength of\na stochastic GAs with the exact convergence ability of any type\nof deterministic local search algorithms such as\nsimulated-annealing, in addition to other nature-inspired\nalgorithms such as ant colony optimization, particle swarm\noptimization etc. The package 'adana' includes a rich working\nenvironment with its many functions that make possible to build\nand work regular GA, adaptive GA, hybrid GA and hybrid adaptive\nGA for any kind of optimization problems. Cebeci, Z. (2021,\nISBN: 9786254397448).", "License": "GPL-3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-09 04:58:35 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2022-02-23 18:50:02 UTC", "RemoteUrl": "https://github.com/cran/adana", "RemoteRef": "HEAD", "RemoteSha": "de348650eb690df18d2e9b3a0b596486be8a737d", "MD5sum": "da0ca71ac5c1f615522fb95ccb3e009e", "_user": "cran", "_type": "src", "_file": "adana_1.1.0.tar.gz", "_fileid": "84d950c4ac48fce8c87cb7f11ed560a5b26831a239992778c4967c7d53c27cd8", "_filesize": 602140, "_sha256": "84d950c4ac48fce8c87cb7f11ed560a5b26831a239992778c4967c7d53c27cd8", "_created": "2026-05-09T04:58:35.000Z", "_published": "2026-05-22T13:40:12.481Z", "_distro": "noble", "_jobs": [ { "job": 77391289071, "time": 116, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "6893010206" }, { "job": 77391289048, "time": 114, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": 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"page": "ax", "title": "Avarage Crossover", "topics": [ "ax" ] }, { "page": "bestsol", "title": "Best solution monitoring function", "topics": [ "bestsol" ] }, { "page": "bin2gray", "title": "Convert from binary to gray code integer", "concept": [ "binary", "integer", "conversion of numbers" ], "topics": [ "bin2gray" ] }, { "page": "bin2int", "title": "Convert Binary Numbers to Integers", "concept": [ "binary", "integer", "conversion of numbers" ], "topics": [ "bin2int" ] }, { "page": "bitmut", "title": "Bit Flip Mutation", "topics": [ "bitmut" ] }, { "page": "blxa", "title": "Blended Crossover (BLX-alpha)", "topics": [ "blxa" ] }, { "page": "blxab", "title": "Blended crossover-alphabeta (BLX-alphabeta)", "topics": [ "blxab" ] }, { "page": "boundmut", "title": "Boundary Mutation", "topics": [ "boundmut" ] }, { "page": "bsearchmut1", "title": "Boundary Search Mutation 1", "topics": [ "bsearchmut1" ] }, { "page": "bsearchmut2", "title": "Boundary Search Mutation 2", "topics": [ 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worst lambda)", "topics": [ "grmuplambda2" ] }, { "page": "grmuplambda3", "title": "Mu+Lambda replacement function 3", "topics": [ "grmuplambda3" ] }, { "page": "grmuplambda4", "title": "Mu+Lambda replacement function 4", "topics": [ "grmuplambda4" ] }, { "page": "grmuvlambda", "title": "Mu & Lambda Replacement Function", "topics": [ "grmuvlambda" ] }, { "page": "grrobin", "title": "Round Robin Replacement Function", "topics": [ "grrobin" ] }, { "page": "hc", "title": "Heuristic Crossover", "topics": [ "hc" ] }, { "page": "hgaoptim", "title": "GA + optim hybridization function", "topics": [ "hgaoptim" ] }, { "page": "hgaoptimx", "title": "GA + optimx hybridization function", "topics": [ "hgaoptimx" ] }, { "page": "hgaroi", "title": "GA + ROI hybridization function", "topics": [ "hgaroi" ] }, { "page": "hux", "title": "Heuristic Uniform Crossover", "topics": [ "hux" ] }, { "page": "icx", "title": "Improved Cycle Crossover (ICX)", "topics": [ "icx" ] }, { "page": "ilmdhc", "title": 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"page": "mpx", "title": "Maximal Preservative Crossover (MPX)", "topics": [ "mpx" ] }, { "page": "mutate", "title": "Function of Mutation Application", "topics": [ "mutate" ] }, { "page": "mx", "title": "Mask crossover", "topics": [ "mx" ] }, { "page": "nunimut", "title": "Non-uniform Mutation", "topics": [ "nunimut" ] }, { "page": "nunimut2", "title": "Adaptive Non-uniform mutation", "topics": [ "nunimut2" ] }, { "page": "ox", "title": "Order Crossover (OX)", "topics": [ "ox" ] }, { "page": "ox2", "title": "Order-based crossover (OX2)", "topics": [ "ox2" ] }, { "page": "pbx", "title": "Position-Based Crossover (PBX)", "topics": [ "pbx" ] }, { "page": "pbx2", "title": "Position-Based Crossover 2 (PBX2)", "topics": [ "pbx2" ] }, { "page": "plotfitness", "title": "Fitness statistics graph by GA generations", "topics": [ "plotfitness" ] }, { "page": "pmx", "title": "Partially Mapped Crossover", "topics": [ "pmx" ] }, { "page": "powmut", "title": "Power Mutation", "topics": [ "powmut" ] }, 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