@misc{10481/78003,
year = {2015},
month = {8},
url = {https://hdl.handle.net/10481/78003},
abstract = {Since their first appearance in 1997 in the prestigious journal Science, algorithm portfolios have become
a popular approach to solve static problems. Nevertheless and despite that success, they have not received
much attention in Dynamic Optimization Problems (DOPs). In this work, we aim at showing these methods
as a powerful tool to solve combinatorial DOPs. To this end, we propose a new algorithm portfolio
for this type of problems that incorporates a learning scheme to select, among the metaheuristics that
compose it, the most appropriate solver or solvers for each problem, configuration and search stage. This
method was tested over 5 binary-coded problems (dynamic variants of OneMax, Plateau, RoyalRoad,
Deceptive and Knapsack) and compared versus two reference algorithms for these problems (Adaptive
Hill Climbing Memetic Algorithm and Self Organized Random Immigrants Genetic Algorithm). The
results showed the importance of a good design of the learning scheme, the superiority of the algorithm
portfolio against the isolated version of the metaheuristics that integrate it, and the competitiveness of its
performance versus the reference algorithms.},
organization = {Spanish Government	TIN2011-27696-C02-01	
TEC2013-45585-C2-2-R},
organization = {Andalusian Government	P11-TIC-8001},
organization = {European Commission},
organization = {Basque Government	PC2013-71A},
organization = {Ibero-American University Association for Post Graduate Studies (AUIP)},
publisher = {Atlantis},
keywords = {Algorithm portfolio},
keywords = {Dynamic optimization problems},
keywords = {Learning},
keywords = {Algorithm selection problem},
keywords = {Combinatorial problems},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {Algorithm portfolio based scheme for dynamic optimization problems},
doi = {10.1080/18756891.2015.1046327},
author = {Fajardo Calderín, Jenny and Pelta Mochcovsky, David Alejandro},
}