Clever Algorithms collects clear, cookbook-style descriptions of nature-inspired optimization and learning methods, organized so you can pick an algorithm and apply it quickly. Each entry follows a consistent template: motivation, strategy, pseudocode, parameter choices, variations, and references, making it easy to compare approaches. The catalog spans evolutionary algorithms, swarm intelligence, immune systems, simulated annealing, tabu search, and other metaheuristics, plus guidance on when and how to tune them. Example implementations and worked problems show how to encode solutions, define fitness, and balance exploration with exploitation. The emphasis is on pragmatism—enough theory to understand why an algorithm works, and enough detail to get it running in your environment. It’s a useful starting point for students and practitioners who want to prototype, benchmark, or hybridize algorithms without digging through scattered academic papers.

Features

  • Contains many algorithms from different families: stochastic, evolutionary, swarm, immune, probabilistic, etc.
  • Presents each algorithm with working code examples (Ruby/Tex/other) to illustrate implementation details
  • Provides standardized descriptions to reduce ambiguity in algorithmic literature
  • Includes appendices/background sections (e.g. Ruby guide, benchmarking)
  • Can be built using LaTeX / Makefile, includes PDF versions, etc.
  • Licensed under Creative Commons Attribution-Noncommercial-Share Alike Australian License, allowing sharing with attribution and non-commercial reuse

Project Samples

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Categories

Machine Learning

License

MIT License

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Ruby

Related Categories

Ruby Machine Learning Software

Registered

2025-09-22