2015/02/26 · Reactive Search advocates the use of simple sub-symbolic machine learning to automate the parameter tuning process and make it an integral (and ...

Most state-of-the-art heuristics are characterized by a certain number of choices and free parameters, whose appropriate setting is a subject that raises ...

Therefore, a reactive heuristic is a technique with the ability of tuning some important parameters during execution by means of a machine learning mechanism.

The main purpose of this paper is to show how using data-mining technique to tackle the problem of tuning the performance of a meta-heuristic search algorithm ...

Reactive Search: Machine Learning For Memory-Based Heuristics / Battiti, Roberto; Brunato, Mauro. - ELETTRONICO. - (2005), pp. 1-20. Tutti ...

Reactive search: machine learning for memory-based heuristics. R Battiti, M Brunato. Handbook of approximation algorithms and metaheuristics, 327-344, 2018. 60 ...

Reactive Search Optimization advocates the adoption of learning mechanisms as an integral part of a heuristic optimization scheme.

Brunato, Reactive Search: Machine Learning for Memory-Based Heuristics, Approximation Algorithms and Metaheuristics, 2005. ... Search Techniques in Artificial ...

The point of view of the book is to look at the zoo of different optimization beasts to underline opportunities for learning and self-tuning strategies.

Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems.