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pyXla documentation
pyXla documentation

Contents:

  • Guides
    • Installation
    • Loading and Sampling
    • Analysing Samples
    • Auxiliary Functions
    • Examples
      • Comparing Continuous Problems
      • Constraints Anaysis
      • Comparing Knapsack Instances
      • Comparing NK Problem Instances
  • Examples by Feature Category
    • Statistical
      • Objective Distribution (distr_F)
      • Violation Distribution (distr_V)
      • Correlation of Values (corr)
      • Correlation of Ranks (corr_ranks)
      • Variable importance (X_imp)
    • Rank-based
      • Pareto Rank Distribution (distr_Par)
      • Deb’s Feasibility Rule Ranking Distribution (distr_Deb)
    • Distance-based
      • Fitness Distance Correlation (FDC)
      • Violation Distance Correlation (VDC)
      • Rank Distance Correlation (RDC)
      • Pairwise Distance Correlation (PDC)
      • Dispersion of the Best Solutions (disp_best)
    • Neighbourhood-based
      • Neighbouring Solutions’ Objective (Fitness) Values Correlation (NFC)
      • Neighbouring Solutions’ Violation Values Correlation (NVC)
      • Neighbouring Solutions’ Ranks Correlation (NRC)
      • Neighbouring Change in Feasibility (N∆Feas)
    • Neural Networks
      • Analysing a NN Problem (Randomly Sampled)
      • Analysing an SGD NN Optimisation Trajectory
  • API Reference
    • pyxla
    • pyxla.util
    • pyxla.sampling
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Neighbourhood-based¶

Features relating to neighbourhood based techniques.

  • Neighbouring Solutions’ Objective (Fitness) Values Correlation (NFC)
  • Neighbouring Solutions’ Violation Values Correlation (NVC)
  • Neighbouring Solutions’ Ranks Correlation (NRC)
  • Neighbouring Change in Feasibility (N∆Feas)
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Neighbouring Solutions’ Objective (Fitness) Values Correlation (NFC)
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Dispersion of the Best Solutions (disp_best)
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