قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| الخوارزمية الجينية لسيناريوهات السياسات× | الخوارزمية الجينية متعددة الأهداف (MOGA)× | |
|---|---|---|
| المجال | المحاكاة | المحاكاة |
| العائلة | Process / pipeline | Process / pipeline |
| سنة النشأة≠ | 1975 (GA); 2000s (policy scenario application) | 1984 |
| صاحب الطريقة≠ | Holland, J. H. (GA foundation); Lempert, Popper & Bankes (policy scenario search) | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| النوع≠ | Evolutionary metaheuristic for policy scenario exploration | Population-based evolutionary optimizer |
| المصدر التأسيسي≠ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. ISBN: 9780262581110 | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| الأسماء البديلة | PSGA, Policy-GA, Policy Optimization Genetic Algorithm, Evolutionary Policy Scenario Search | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| ذات صلة | 4 | 4 |
| الملخص≠ | 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. | A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among. |
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