Evolutionary policy iteration under a sampling regime for stochastic combinatorial optimization

Lauren A. Hannah, Warren Buckler Powell

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


This article modifies the evolutionary policy selection algorithm of Chang et al., [1], [2], which was designed for use in infinite horizon Markov decision processes (MDPs) with a large action space to a discrete stochastic optimization problem, in an algorithm called Evolutionary Policy Iteration-Monte Carlo (EPI-MC). EPI-MC allows EPI to be used in a stochastic combinatorial optimization setting with a finite action space and a noisy cost (value) function by introducing a sampling schedule. Convergence of EPI-MC to the optimal action is proven and experimental results are given.

Original languageEnglish (US)
Article number5409644
Pages (from-to)1254-1257
Number of pages4
JournalIEEE Transactions on Automatic Control
Issue number5
StatePublished - May 2010

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Combinatorial optimization
  • Evolutionary policy iteration (EPI)
  • Monte Carlo (MC)
  • Stochastic optimization


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