Linganisha mbinu

	Policy Scenario Genetic Algorithm	Uboreshaji wa Malengo Mengi wa Hali ya Sera
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1975 (GA); 2000s (policy scenario application)	1990s–2000s
Mwanzilishi	Holland, J. H. (GA foundation); Lempert, Popper & Bankes (policy scenario search)	Evolved from multi-objective optimization and policy scenario analysis communities
Aina	Evolutionary metaheuristic for policy scenario exploration	Scenario-conditioned multi-objective search
Chanzo asilia	Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. ISBN: 9780262581110	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396
Majina mbadala	PSGA, Policy-GA, Policy Optimization Genetic Algorithm, Evolutionary Policy Scenario Search	PS-MOO, Policy-Driven MOO, Scenario-Based Multi-Objective Optimization, Policy MOO
Zinazohusiana	4	4
Muhtasari	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.	Policy Scenario Multi-Objective Optimization (PS-MOO) integrates explicit policy scenario construction with multi-objective optimization to identify Pareto-optimal policy options across plausible future states. Decision-makers evaluate trade-offs between competing objectives — such as economic efficiency, equity, and environmental impact — for each distinct policy scenario, then compare Pareto fronts to select robust or scenario-contingent strategies.