পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| এজেন্ট-ভিত্তিক জেনেটিক অ্যালগরিদম× | জেনেটিক অ্যালগরিদম× | |
|---|---|---|
| ক্ষেত্র≠ | অনুকরণ | অনুকূলকরণ |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1990s | 1975 |
| প্রবর্তক≠ | Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s | John Henry Holland |
| ধরন≠ | Hybrid evolutionary-agent simulation | Population-based metaheuristic |
| মৌলিক উৎস≠ | Adamidis, P., & Petridis, V. (1996). Co-operating populations with different evolution behaviors. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC 1996), 188-191. IEEE. link ↗ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| অপর নাম≠ | ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| সম্পর্কিত | 5 | 5 |
| সারসংক্ষেপ≠ | An Agent-Based Genetic Algorithm (ABGA) partitions a genetic algorithm's population across a network of autonomous agents, each maintaining a local sub-population and evolving it independently. Agents periodically exchange individuals (migration) based on proximity or communication rules, enabling parallel exploration of the search space while preserving population diversity and avoiding premature convergence. | 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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