Intelligent Optimization

Optimization plus Machine Learning for Reliable Artificial Intelligence
Roberto Battiti, Kevin Tierney and Mauro Brunato

Cartoons are courtesy of Marco Dianti.

The strong coupling between Mathematical Optimization and Machine Learning pioneered by LION is rapidly gaining momentum in different research and business projects.

This book presents two topics which are in most cases separated: Machine Learning (the design of flexible models from data) and Intelligent Optimization (the automated creation and selection of optimal or improving solutions). More and more innovative people can now master the source of power arising from LION techniques to solve problems, improve businesses, create new applications.

Powerful tools are not only for cognoscenti and this book does a serious effort to distinguish the paradigm shift brought about by machine learning and intelligent optimization methods from the fine details, and it does not refrain from presenting concrete examples and vivid sticky images (made to stick to your mind).

Get the IObook

Our book will be available on this web site starting June 10, 2026 at 12:00pm GMT.

Contact us by email (roberto.battiti ((AT)) intelligent-optimization.org subject: IObook) if you would like to be alerted when the book is released.

Please cite our book as:
Roberto Battiti, Kevin Tierney, Mauro Brunato
Intelligent Optimization — Optimization plus Machine Learning for Reliable Artificial Intelligence.
LION association, Italy, 2026.
[ BibTeX ]
@book {
   author = "Roberto Battiti and Kevin Tierney and Mauro Brunato",
   title = "Intelligent Optimization - Optimization plus Machine Learning for Reliable Artificial Intelligence.",
   publisher = "LION association, Italy",
   year = "2026",
   url = "http://intelligent-optimization.org/iobook/"
}

Tutorials

The book will be accompanied by the periodic release of tutorials about topics related to intelligent optimization. The tutorials, written in Python, are presented as static web pages; however, the source Jupyter notebooks are provided to encourage readers to explore different scenarios and test their own ideas.

So far, the available topics are:

Combinatorial Optimization

You will be guided through a series of tentative solutions of the Vehicle Routing Problem, from simple greedy constructions to more and more effective ways to refine and improve them.

• Greedy Algorithms, part 1: introduction to the Vehicle Routing Problem and to building simple solutions.
• Greedy Algorithms, part 2: refining a constructive heuristic.
• Coming next — local search algorithms: improving existing solutions via small changes.

Intelligent Optimization

Problems at the crossroads of Machine Learning and Optimization Heuristics: a problem will be introduced and a solution based on Intelligent Optimization principles will be developed step by step.

• Learning to park a car: a control task carried out by a neural network trained by a continuous oprimization algorithm.