পদ্ধতির তুলনা করুন

	হাইপার-হিউরিস্টিকস	জেনেটিক অ্যালগরিদম	ম্যাথিউরিস্টিকস: গাণিতিক প্রোগ্রামিং এবং মেটাহিউরিস্টিকসের সংমিশ্রণ
ক্ষেত্র	অনুকূলকরণ	অনুকূলকরণ	অনুকূলকরণ
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2013	1975	2009
প্রবর্তক	Burke et al.	John Henry Holland	Maniezzo, Stützle & Voß
ধরন	High-level search methodology	Population-based metaheuristic	Hybrid optimization framework
মৌলিক উৎস	Burke, E. K., et al. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695–1724.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Maniezzo, V., Stützle, T., & Voß, S. (Eds.). (2009). Matheuristics: Hybridizing Metaheuristics and Mathematical Programming. Springer. ISBN: 978-1-4419-1305-0
অপর নাম	Heuristic of Heuristics, Algorithm Selection Hyper-Heuristic, Selection Hyper-Heuristic, Hiyer-Sezgisel	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Hybrid Metaheuristics, MIP-based Heuristics, Math-Programming Hybrids, Matematiksel Sezgisel Yöntemler
সম্পর্কিত	3	5	3
সারসংক্ষেপ	Hyper-heuristics are high-level methodologies that search over a space of heuristics rather than directly over the space of solutions. Introduced systematically by Burke et al. (2013) in their landmark survey, hyper-heuristics operate by selecting or generating low-level heuristics to solve hard combinatorial optimisation and search problems, aiming to automate the design of optimisation algorithms across diverse problem domains without requiring deep problem-specific knowledge.	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.	Matheuristics is a class of hybrid optimization methods that tightly couple exact mathematical programming components—such as mixed-integer programming (MIP) solvers—with metaheuristic search procedures. Formally introduced and named by Maniezzo, Stützle, and Voß in 2009, the framework leverages the global-search capability of metaheuristics and the structural exploitation of mathematical programming to tackle large-scale combinatorial optimization problems that neither approach can solve effectively alone.