@inproceedings{KATAYAMA-GECCO-2000,
title = {Solving large binary quadratic programming problems by effective genetic local search algorithm},
author = {Kengo Katayama and Masafumi Tani and Hiroyuki Narihisa},
url = {https://dl.acm.org/doi/abs/10.5555/2933718.2933833},
year = {2000},
date = {2000-07-10},
urldate = {2000-07-10},
booktitle = {GECCO 2000: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation},
pages = {643-650},
abstract = {A genetic local search (GLS) algorithm, which is a combination technique of genetic algorithm and local search, for the unconstrained binary quadratic programming problem (BQP) is presented. An effective local search algorithm, which is a variant of the k-opt local search for the BQP by Merz et al., is described, and the performance of the GLS with the variant local search heuristic is demonstrated on several large-scale problem instances. Our computational results indicate that the GLS is able to frequently find the best-known solution with a relatively short running time and obviously our average solution values obtained are better than previous powerful heuristic approaches especially for the large problem instances of 2,500 variables.},
keywords = {Genetic local search, Metaheuristics, Unconstrained binary quadratic programming problem},
pubstate = {published},
tppubtype = {inproceedings}
}
