Welcome to pySBO’s documentation!

pySBO is a Python platform for Surrogate-Based Optimization.

Contents:

• About pySBO
  • Notes
  • Support
  • Background on Surrogate-Based Optimization
  • Author
  • License
  • Supporting institutions
  • Collaborators
• Installation
  • Linux
  • Windows
• pySBO Packages
  • Problems
    • Classes summary
    • Abstract classes
    • Single-Objective Problems
    • Multi-Objective Problems
    • Design of Experiments
  • Evolution
    • Classes summary
    • Population
    • Mutation
    • Crossover
    • Selection
    • Replacement
    • Reference Vector Set
  • Evolution Controls
    • Classes summary
    • Naive
    • Informed
    • Ensemble
  • Surrogates
    • Classes summary
    • Surrogate (abstract)
    • Approximated Bayesian Neural Network
    • Bayesian Linear Regressor
    • Gaussian Processes
• Algorithms
  • Evolutionary Algorithm (surrogate-free)
  • Surrogate-Assisted Evolutionary Algorithm
  • Surrogate-Driven Algorithms
    • q-EGO with Surrogate Believer
    • q-EGO with Constant Liar
    • q-Pareto
    • q-PostHMC
    • q-subnets
  • Hybrid Algorithms
    • Surrogate-Model-Based Optimization + Evolutionary Algorithm
    • Hybrid Concurrent Acquition Process
    • Hybrid Successive Acquition Process
  • Multi-Objective Algorithms
    • Non-Domiated Sorted Genetic Algorithm
    • Reference Vector Guided Evolutionary Algorithm
    • RVEA*
    • Adaptive Bayesian Multi-Objective Evolutionary Algorithm
    • Surrogate-Assisted Evolutionary Algorithm for Medium Scale Expensive problems
  • Synchronous Parallel Evaluations

Indices and tables

• Index

• Module Index

• Search Page