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Version: 0.6.1.3
  • pymoo: Multi-objective Optimization in Python
  • News
  • Installation
  • Getting Started
    • Preface: Basics and Challenges
    • Part I: A Constrained Bi-objective Optimization Problem
    • Part II: Find a Solution Set using Multi-objective Optimization
    • Part III: Multi-Criteria Decision Making
    • Part IV: Analysis of Convergence
    • Part V: Some more useful Information
    • Source Code
  • Interface
    • Minimize
    • Problem
    • Algorithm
    • Termination Criterion
    • Callback
    • Display
    • Result
  • Problems
    • Definition
    • Test Problems
      • Ackley
      • Griewank
      • Zakharov
      • Rastrigin
      • Rosenbrock
      • BNH
      • ZDT
      • OSY
      • TNK
      • Truss2D
      • Welded Beam
      • Omni-test
      • SYM-PART
      • DTLZ
      • WFG
      • MW
      • DAS-CMOP
      • MODAct
      • DF: Benchmark Problems for CEC2018 Competition on Dynamic Multiobjective Optimisation
    • Parallelization
  • Algorithms
    • Initialization
    • Usage
    • List Of Algorithms
    • Hyperparameters
    • GA: Genetic Algorithm
    • BRKGA: Biased Random Key Genetic Algorithm
    • DE: Differential Evolution
    • Nelder Mead
    • PSO: Particle Swarm Optimization
    • Pattern Search
    • ES: Evolutionary Strategy
    • SRES: Stochastic Ranking Evolutionary Strategy
    • ISRES: Improved Stochastic Ranking Evolutionary Strategy
    • CMA-ES
    • G3PCX: A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization
    • NSGA-II: Non-dominated Sorting Genetic Algorithm
    • R-NSGA-II
    • NSGA-III
    • U-NSGA-III
    • R-NSGA-III
    • MOEA/D
    • C-TAEA
    • AGE-MOEA: Adaptive Geometry Estimation based MOEA
    • AGE-MOEA2: Adaptive Geometry Estimation based MOEA
    • RVEA: Reference Vector Guided Evolutionary Algorithm
    • SMS-EMOA: Multiobjective selection based on dominated hypervolume
    • D-NSGA-II: Dynamic Multi-Objective Optimization Using Modified NSGA-II
    • KGB-DMOEA: Knowledge-Guided Bayesian Dynamic Multi-Objective Evolutionary Algorithm
  • Constraint Handling
    • Constrained Problem
    • Feasbility First (Parameter-less Approach)
    • Constraint Violation (CV) as Penalty
    • Constraint Violation (CV) As Objective
    • \(\epsilon\) -Constraint Handling
    • Repair Operator
  • Gradients
  • Customization
    • Binary Variable Problem
    • Discrete Variable Problem
    • Permutations
    • Mixed Variable Problem
    • Custom Variable Type
    • Biased Initialization
    • Subset Selection Problem
  • Operators
    • Sampling
    • Selection
    • Crossover
    • Mutation
    • Repair
  • Visualization
    • Scatter Plot
    • Parallel Coordinate Plots
    • Heatmap
    • Petal Diagram
    • Radar Plot
    • Radviz
    • Star Coordinate Plot
    • Video
  • Multi-Criteria Decision Making (MCDM)
  • Case Studies
    • Subset Selection Problem
    • Portfolio Allocation
    • Matrix Inversion
  • Performance Indicator
  • Miscellaneous
    • Reference Directions
    • Convergence
    • Decomposition
    • Karush Kuhn Tucker Proximity Measure (KKTPM)
    • Checkpoints
  • FAQ
  • API Reference
    • Model
    • Algorithms
  • Versions
  • Contribute
  • Cite Us
  • Contact
  • License

CustomizationΒΆ

  • Binary Variable Problem
  • Discrete Variable Problem
  • Permutations
  • Mixed Variable Problem
  • Custom Variable Type
  • Biased Initialization
  • Subset Selection Problem

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