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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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sezgi-0.1.3-cp39-abi3-win_amd64.whl CPython 3.9 abi3 Windows x86-64 Details
sezgi-0.1.3-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.9 abi3 Linux glibc 2.17+ x86-64 Details
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