Methods › General › Stochastic Optimization › HGS
Hunger Games Search
HGS
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Hunger Games Search (HGS) is a general-purpose population-based optimization technique with a simple structure, special stability features and very competitive performance to realize the solutions of both constrained and unconstrained problems more effectively. HGS is designed according to the hunger-driven activities and behavioural choice of animals. This dynamic, fitness-wise search method follows a simple concept of “Hunger” as the most crucial homeostatic motivation and reason for behaviours, decisions, and actions in the life of all animals to make the process of optimization more understandable and consistent for new users and decision-makers. The Hunger Games Search incorporates the concept of hunger into the feature process; in other words, an adaptive weight based on the concept of hunger is designed and employed to simulate the effect of hunger on each search step. It follows the computationally logical rules (games) utilized by almost all animals and these rival activities and games are often adaptive evolutionary by securing higher chances of survival and food acquisition. This method's main feature is its dynamic nature, simple structure, and high performance in terms of convergence and acceptable quality of solutions, proving to be more efficient than the current optimization methods.
Implementation of the HGS algorithm is available at https://aliasgharheidari.com/HGS.html.
Code snippet in the archive: a link on aliasgharheidari.com (archive link, not checked and not linked: not a code host this site links to).
Papers archive 2025-07-28
12 shown of 12, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Masked Language Models are Good Heterogeneous Graph Generalizers 6 Jun 2025 · 1 repository · arXiv:2506.06157
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Pushing Rendering Boundaries: Hard Gaussian Splatting 6 Dec 2024 · 0 repositories · arXiv:2412.04826
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Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs 7 Jan 2024 · 0 repositories · arXiv:2401.03597
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Cross-heterogeneity Graph Few-shot Learning 10 Aug 2023 · 0 repositories · arXiv:2308.05275
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Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows 21 Jul 2023 · 0 repositories · arXiv:2307.11800
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Homophily-oriented Heterogeneous Graph Rewiring 13 Feb 2023 · 0 repositories · arXiv:2302.06299
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PAC learning and stabilizing Hedonic Games: towards a unifying approach 31 Jan 2023 · 0 repositories · arXiv:2301.13756
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Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill? 9 Sep 2022 · 0 repositories · arXiv:2210.12075
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Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks 3 Mar 2022 · 1 repository · arXiv:2203.02018
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Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning 31 Aug 2021 · 0 repositories · arXiv:2108.13886
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Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood 23 Nov 2020 · 2 repositories · arXiv:2012.10384
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Are mouse and cat the missing link in the COVID-19 outbreaks in seafood markets? 18 Sep 2020 · 0 repositories · arXiv:2009.09911
Tasks archive 2025-07-28
19 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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