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  "Title": "Multi-Objective Minimum Spanning Tree via NSGA-II with Local\nSearch",
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  "Authors@R": "c(\nperson(given  = \"Jorge A.\",\nfamily = \"Parraga-Alava\",\nemail  = \"jorge.parraga@utm.edu.ec\",\nrole   = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-8558-9122\")))",
  "Description": "Solves the Multi-Criteria Minimum Spanning Tree (mc-MST)\nproblem on complete weighted graphs by combining the\nNon-dominated Sorting Genetic Algorithm II (NSGA-II) with\noptional Pareto local search operators. Chromosomes are\nrepresented as Prufer sequences so that every random individual\ndecodes to a valid spanning tree (Cayley's theorem), avoiding\nrepair operators. Four solver variants are provided: pure\nNSGA-II (\"base\"), Path Relinking (\"PR\"), Pareto Local Search\n(\"PLS\"), and Tabu Search (\"TS\"). The package supports 2 and 3\nobjective formulations and provides convenience functions to\nplot Pareto fronts and best-compromise spanning trees. This\npackage is the reference implementation of the method described\nin Parraga-Alava, Inostroza-Ponta and Dorn (2017)\n<doi:10.1109/CEC.2017.7969432>.",
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    "tabu_search",
    "tournament_selection",
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    {
      "page": "momst-package",
      "title": "momst: Multi-Objective Minimum Spanning Tree via NSGA-II with Local Search",
      "topics": [
        "momst-package",
        "momst"
      ]
    },
    {
      "page": "apply_local_search",
      "title": "Apply the Configured Local-Search Variant",
      "topics": [
        "apply_local_search"
      ]
    },
    {
      "page": "build_weight_lookup",
      "title": "Pre-build Edge-Weight Lookup Matrices",
      "topics": [
        "build_weight_lookup"
      ]
    },
    {
      "page": "compute_objectives",
      "title": "Compute Multi-Objective Costs for a Population",
      "topics": [
        "compute_objectives"
      ]
    },
    {
      "page": "decode_prufer",
      "title": "Decode a Prufer Sequence to its Spanning Tree (Linear Time)",
      "topics": [
        "decode_prufer"
      ]
    },
    {
      "page": "generate_instance",
      "title": "Generate a Complete-Graph Instance for MO-MST",
      "topics": [
        "generate_instance"
      ]
    },
    {
      "page": "generate_prufer_population",
      "title": "Generate an Initial Prufer-Encoded Population",
      "topics": [
        "generate_prufer_population"
      ]
    },
    {
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      "title": "Assign Pareto Rank and Crowding Distance",
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      "title": "Pareto Local Search",
      "topics": [
        "pareto_local_search"
      ]
    },
    {
      "page": "path_relinking",
      "title": "Path Relinking on the Current Pareto Front",
      "topics": [
        "path_relinking"
      ]
    },
    {
      "page": "plot_best_tree",
      "title": "Plot the Best-Compromise Spanning Tree",
      "topics": [
        "plot_best_tree"
      ]
    },
    {
      "page": "plot_pareto_front",
      "title": "Plot a Pareto Front (2-objective case)",
      "topics": [
        "plot_pareto_front"
      ]
    },
    {
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      "title": "Random Mutation on Prufer Sequences",
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      ]
    },
    {
      "page": "run_momst",
      "title": "Run the MO-MST NSGA-II Solver",
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        "run_momst"
      ]
    },
    {
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      "title": "Tabu Search on the Current Pareto Front",
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        "tabu_search"
      ]
    },
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      "title": "Tournament Selection",
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      "author": "Jorge A. Parraga-Alava",
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        "Reference",
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        "Visual comparison of the four fronts",
        "One panel per variant",
        "All variants superposed",
        "A unified \"ground truth\" front",
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        "Step 4. Plot the Pareto front",
        "Step 5. Decode and plot the best-compromise tree",
        "Working with three objectives",
        "Reproducibility",
        "Where to go next"
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