sezgi

sezgi (Turkish for "intuition") is a Rust-core, component-based metaheuristic optimization library with Python and R frontends, built for deterministic, cross-language, bit-exact reproducibility of algorithm runs.

  • 29 built-in algorithm classes (genetic/evolutionary, swarm, physics-inspired, local search), each citing its source in its own docstring.
  • A subclassable class hierarchy (Algorithm, PopulationAlgorithm, LocalSearch, Problem): your Python generate()/vary()/neighbor() hook runs as a callback inside the Rust engine loop.
  • Deterministic by construction: a run is fully determined by (spec, problem, seed, budget), and the same seed reproduces byte-identical results across the Python and R frontends.
  • Benchmarking built in: BBOB and CEC 2014/2017/2022 suites, IOH-format logging, ECDF curves, COCO export, and a statistical-comparison toolkit (Friedman, Wilcoxon+Holm, Cliff's delta, Bayesian signed-rank, Plackett-Luce).
  • Multi-objective optimization via NSGA-II over ZDT/DTLZ/WFG, with hypervolume and IGD indicators.

Quickstart

import sezgi

problem = sezgi.bbob(1, 5, 1)  # BBOB f1 (Sphere), dim=5, instance=1
result = sezgi.GeneticAlgorithm(pop_size=20).run(problem, budget=1000, seed=1)
print(result.evals_used, result.best_f, result.gap)

Documentation

Full documentation — installation, a six-page introductory Learn track, eight executed tutorials, architecture pages, and the complete API reference — is at https://tdelphi1981.github.io/sezgi/.

License

MIT. Developed at the Department of Computer Science, Karadeniz Technical University. Source code: https://github.com/tdelphi1981/sezgi. Third-party notices for vendored benchmark data and quoted reference-source excerpts: see THIRD-PARTY-NOTICES.md.

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Credits

Author: Beyzanur Siyah, Emir Karayağız

GitHub Statistics

Data verified by PyPI on Sep 5, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.9

Tags

metaheuristics optimization evolutionary-algorithms benchmarking reproducibility

Classifiers

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