Process / pipelineSimulation / optimization

Policy Scenario Genetic Algorithm — Evolutionary Search over Policy Alternative Spaces

The Policy Scenario Genetic Algorithm applies evolutionary search to systematically explore large, combinatorial policy alternative spaces under multiple future scenarios. Rather than exhaustively enumerating options, it breeds successive generations of candidate policies, retaining those that perform well across scenario conditions, yielding robust, high-performing policy recommendations.

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Sources

  1. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. ISBN: 9780262581110
  2. Lempert, R. J., Popper, S. W., & Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation, Santa Monica, CA. link

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Referenced by

ScholarGatePolicy Scenario Genetic Algorithm (Policy Scenario Genetic Algorithm — Evolutionary Search over Discrete Policy Alternative Spaces). Retrieved 2026-06-04 from https://scholargate.app/en/simulation/policy-scenario-genetic-algorithm