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Everything about Optimal computing budget allocation

In Computer Science, Optimal Computing Budget Allocation (OCBA) is a simulation optimization method designed to maximize the Probability of Correct Selection (PCS) while minimizing computational costs. First introduced by Dr. Chun-Hung Chen in the mid-1990s, OCBA determines how many simulation runs (or how much computational time) or the number of replications each design alternative needs to identify the best option while using as few resources as possible. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Optimal computing budget allocationA 5-minute orientation
  2. Skim a recent paper: An Optimal Computing Budget Allocation Tree Policy for Monte Carlo Tree SearchInfluential work since 2022
  3. Pick up a classic book: Pension financeThe most-reprinted book on the subject
  4. Read the landmark paper: Optimal computing budget allocation for the vector evaluated genetic algorithm in multi-objective simulation optimizationCited 119 times
  5. Go sideways: Bilevel optimizationA closely related topic
  6. Go sideways: Mathematical optimizationA closely related topic

📖 Definitions

No dictionary entry for this exact phrase.

🗂️ The subject at a glance

  1. Intuitive Explanation
  2. Core Optimization Problem
  3. Some extensions of OCBA
  4. Multi-Objective OCBA
  5. Constrained optimization
  6. Feasibility determination
  7. OCBA with expected opportunity cost
  8. OCBA with input uncertainty
  9. Recent Applications of OCBA
  10. Emerging Research Area: Integration of Machine Learning with OCBA

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics