Clever algorithms
Implementing an Artificial Intelligence algorithm is difficult. Algorithm descriptions may be incomplete, inconsistent, and distributed across a number of papers, chapters and even websites. This can result in varied interpretations of algorithms, undue attrition of algorithms, and ultimately bad science. This book is an effort to address these issues by providing a handbook of algorithmic recipes drawn from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence, described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.--Back cover.
Stats for nerds
2recommend
4mentions
4people
2011–2025years
50% recommend50% neutral
Mentions per year
What Hacker News says
- Some additional optimization resources (for metaheuristics, where you only have the objective/score function and no derivative):
- "Essentials of Metaheuristics" by Sean Luke https://cs.gmu.edu/~sean/book/metaheuristics/
- "Clever Algorithms" by Jason Brownlee https://cleveralgorithms.com/
Timefold uses the metaheuristic algorithms in these books (Tabu Search, Late Acceptance, Simulated Annealing, etc.) to find near-optimal solutions quickly from a score function (typically defined in a Java stream-like/SQL-like syntax so score calculation can be done incrementally to improve score calculation speed).
You can see simplified diagrams of these algorithms in action in Timefold's docs: https://docs.timefold.ai/timefold-solver/latest/optimization....
Disclosure: I work for Timefold.
- Here's my list:
Structure & Interpretation of Computer Programs (Abelman, Sussman - Lisp oriented, classic) Algorithms in a Nutshell (OReilly) Clever Algorithms (Jason Brownlee. Uses Ruby for examples) Data Mining Analysis - Fundamental Algorithms (Mo. Zaki - ebook) Python for Data Analysis (Wes McKinney) Mining of Massive Datasets (Rajaraman, Leskovec, Ullman - ebook)
Enjoy.
- Wow, happy to see this posted here again. Just found out that the book is finally available on Amazon (yay!) http://www.amazon.com/Clever-Algorithms-Nature-Inspired-Prog...
Happy to answer any questions about the book/content.
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