By Topic

R-EVO: A Reactive Evolutionary Algorithm for the Maximum Clique Problem

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Mauro Brunato ; Department of Information Engineering and Computer Science, University of Trento, Trento, Italy ; Roberto Battiti

An evolutionary algorithm with guided mutation (EA/G) has been proposed recently for solving the maximum clique problem. In the framework of estimation-of-distribution algorithms, guided mutation uses a model distribution to generate offspring by combining the local information of solutions found so far with global statistical information. Each individual is then subjected to a Marchiori's repair heuristic, based on randomized extraction and greedy expansion, to ensure that it represents a legal clique. A novel reactive and evolutionary algorithm (R-EVO) proposed in this paper starts from the same evolutionary framework but considers more complex individuals, which modify tentative solutions by local search with memory, according to the reactive search optimization (RSO) principles. In particular, the estimated distribution is used to periodically initialize the state of each individual based on the previous statistical knowledge extracted from the population. We demonstrate that the combination of the estimation-of-distribution concept with RSO produces significantly better results than EA/G for many test instances and it is remarkably robust with respect to the setting of the algorithm parameters. R-EVO adopts a drastically simplified low-knowledge version of reactive local search (RLS), with a simple internal diversification mechanism based on tabu-search, with a prohibition parameter proportional to the estimated best clique size. R-EVO is competitive with the more complex full-knowledge RLS-EVO that adopts the original RLS algorithm. For most of the benchmark instances, the hybrid scheme version produces significantly better results than EA/G for comparable or a smaller central processing unit time.

Published in:

IEEE Transactions on Evolutionary Computation  (Volume:15 ,  Issue: 6 )