{
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  "Title": "Core Mathematical Functions for Multi-Objective Optimization",
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  "Description": "Fast implementations of mathematical operations and\nperformance metrics for multi-objective optimization, including\nfiltering and ranking of dominated vectors according to Pareto\noptimality, hypervolume metric, C.M. Fonseca, L. Paquete, M.\nLópez-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon\nindicator, inverted generational distance, computation of the\nempirical attainment function, V.G. da Fonseca, C.M. Fonseca,\nA.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, and Vorob'ev\nthreshold, expectation and deviation, M. Binois, D.\nGinsbourger, O. Roustant (2015)\n<doi:10.1016/j.ejor.2014.07.032>, among others.",
  "License": "LGPL (>= 2.1)",
  "Copyright": "file COPYRIGHTS",
  "URL": "https://multi-objective.github.io/moocore/r/,\nhttps://github.com/multi-objective/moocore",
  "BugReports": "https://github.com/multi-objective/moocore/issues",
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  "Repository": "https://multi-objective.r-universe.dev",
  "Date/Publication": "2026-05-22 13:36:03 UTC",
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  "Author": "Manuel López-Ibáñez [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-9974-1295>),\nCarlos Fonseca [ctb],\nLuís Paquete [ctb],\nAndreia P. Guerreiro [ctb],\nMickaël Binois [ctb],\nMichael H. Buselli [cph] (AVL-tree library),\nWessel Dankers [cph] (AVL-tree library),\nNumPy Developers [cph] (RNG and ziggurat constants),\nJean-Sebastien Roy [cph] (mt19937 library),\nMakoto Matsumoto [cph] (mt19937 library),\nTakuji Nishimura [cph] (mt19937 library)",
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    "compute_eafdiff_call",
    "eaf",
    "eaf_as_list",
    "eafdiff",
    "epsilon_additive",
    "epsilon_mult",
    "filter_dominated",
    "generate_ndset",
    "hv_approx",
    "hv_contributions",
    "hypervolume",
    "igd",
    "igd_plus",
    "is_nondominated",
    "largest_eafdiff",
    "normalise",
    "pareto_rank",
    "r2_exact",
    "rbind_datasets",
    "read_datasets",
    "total_whv_rect",
    "transform_maximise",
    "vorob_dev",
    "vorob_t",
    "whv_hype",
    "whv_rect",
    "write_datasets"
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      "title": "Conditional Pareto fronts obtained from Gaussian processes simulations.",
      "object": "CPFs",
      "class": [
        "data.frame"
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        "f1",
        "f2",
        "set"
      ],
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      "table": true,
      "tojson": true
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      "title": "Results of Hybrid GA on Vanzyl and Richmond water networks",
      "object": "HybridGA",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "SPEA2minstoptimeRichmond",
      "title": "Results of SPEA2 when minimising electrical cost and maximising the minimum idle time of pumps on Richmond water network.",
      "object": "SPEA2minstoptimeRichmond",
      "class": [
        "data.frame"
      ],
      "fields": [
        "V1",
        "V2",
        "set"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "SPEA2relativeRichmond",
      "title": "Results of SPEA2 with relative time-controlled triggers on Richmond water network.",
      "object": "SPEA2relativeRichmond",
      "class": [
        "data.frame"
      ],
      "fields": [
        "V1",
        "V2",
        "set"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "SPEA2relativeVanzyl",
      "title": "Results of SPEA2 with relative time-controlled triggers on Vanzyl's water network.",
      "object": "SPEA2relativeVanzyl",
      "class": [
        "data.frame"
      ],
      "fields": [
        "V1",
        "V2",
        "set"
      ],
      "rows": 107,
      "table": true,
      "tojson": true
    },
    {
      "name": "tpls50x20_1_MWT",
      "title": "Various strategies of Two-Phase Local Search applied to the Permutation Flowshop Problem with Makespan and Weighted Tardiness objectives.",
      "object": "tpls50x20_1_MWT",
      "class": [
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      ],
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        "Makespan",
        "WeightedTardiness",
        "run"
      ],
      "rows": 1511,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "as_double_matrix",
      "title": "Convert input to a matrix with '\"double\"' storage mode ('base::storage.mode()').",
      "topics": [
        "as_double_matrix"
      ]
    },
    {
      "page": "attsurf2df",
      "title": "Convert a list of attainment surfaces to a single EAF 'data.frame'.",
      "concept": [
        "eaf"
      ],
      "topics": [
        "attsurf2df"
      ]
    },
    {
      "page": "choose_eafdiff",
      "title": "Interactively choose according to empirical attainment function differences",
      "concept": [
        "eaf"
      ],
      "topics": [
        "choose_eafdiff"
      ]
    },
    {
      "page": "compute_eaf_call",
      "title": "Same as 'eaf()' but performs no checks and does not transform the input or the output. This function should be used by other packages that want to avoid redundant checks and transformations.",
      "concept": [
        "eaf"
      ],
      "topics": [
        "compute_eaf_call"
      ]
    },
    {
      "page": "compute_eafdiff_call",
      "title": "Same as 'eafdiff()' but performs no checks and does not transform the input or the output. This function should be used by other packages that want to avoid redundant checks and transformations.",
      "concept": [
        "eaf"
      ],
      "topics": [
        "compute_eafdiff_call"
      ]
    },
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      "title": "Conditional Pareto fronts obtained from Gaussian processes simulations.",
      "topics": [
        "CPFs"
      ]
    },
    {
      "page": "eaf",
      "title": "Exact computation of the Empirical Attainment Function (EAF)",
      "concept": [
        "eaf"
      ],
      "topics": [
        "eaf"
      ]
    },
    {
      "page": "eaf_as_list",
      "title": "Convert an EAF data frame to a list of data frames, where each element of the list is one attainment surface. The function 'attsurf2df()' can be used to convert the list into a single data frame.",
      "concept": [
        "eaf"
      ],
      "topics": [
        "eaf_as_list"
      ]
    },
    {
      "page": "eafdiff",
      "title": "Compute empirical attainment function differences",
      "concept": [
        "eaf"
      ],
      "topics": [
        "eafdiff"
      ]
    },
    {
      "page": "epsilon",
      "title": "Epsilon metric",
      "concept": [
        "metrics"
      ],
      "topics": [
        "epsilon",
        "epsilon_additive",
        "epsilon_mult"
      ]
    },
    {
      "page": "generate_ndset",
      "title": "Generate a random set of mutually nondominated points",
      "topics": [
        "generate_ndset"
      ]
    },
    {
      "page": "hv_approx",
      "title": "Approximate the hypervolume indicator.",
      "concept": [
        "metrics"
      ],
      "topics": [
        "hv_approx"
      ]
    },
    {
      "page": "hv_contributions",
      "title": "Hypervolume contribution of a set of points",
      "concept": [
        "metrics"
      ],
      "topics": [
        "hv_contributions"
      ]
    },
    {
      "page": "HybridGA",
      "title": "Results of Hybrid GA on Vanzyl and Richmond water networks",
      "topics": [
        "HybridGA"
      ]
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