পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| পলিসি সিনারিও জেনেটিক অ্যালগরিদম× | জেনেটিক অ্যালগরিদম× | |
|---|---|---|
| ক্ষেত্র≠ | অনুকরণ | অনুকূলকরণ |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1975 (GA); 2000s (policy scenario application) | 1975 |
| প্রবর্তক≠ | Holland, J. H. (GA foundation); Lempert, Popper & Bankes (policy scenario search) | John Henry Holland |
| ধরন≠ | Evolutionary metaheuristic for policy scenario exploration | Population-based metaheuristic |
| মৌলিক উৎস≠ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. ISBN: 9780262581110 | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| অপর নাম≠ | PSGA, Policy-GA, Policy Optimization Genetic Algorithm, Evolutionary Policy Scenario Search | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| সম্পর্কিত≠ | 4 | 5 |
| সারসংক্ষেপ≠ | 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 genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail. |
| ScholarGateডেটাসেট ↗ |
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