@techreport{TD:101106,
	att_abstract={{Heuristics for combinatorial optimization are often controlled by
discrete and continuous parameters that define its behavior.  The number
of possible configurations of the heuristic can be large, resulting in
a difficult analysis.  Manual tuning can be time-consuming, and usually
considers a very limited number of configurations.  An alternative to
manual tuning is automatic tuning. In this paper, we present a scheme for
automatic tuning of GRASP with evolutionary path-relinking heuristics. The
proposed scheme uses a biased random-key genetic algorithm (BRKGA) to
determine good configurations. We illustrate the tuning procedure with
experiments on three optimization problems: set covering, maximum cut, and
node capacitated graph partitioning. For each problem we automatically
tune a specific GRASP with evolutionary path-relinking heuristic to
produce fast effective procedures.
}},
	att_authors={mr5626},
	att_categories={C_CCF.1, C_CCF.2, C_CCF.7, C_CCF.8},
	att_copyright={{Springer}},
	att_copyright_notice={{The definitive version was published in   2013. {{, Volume 7919}}{{, 2013-05-31}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={Randomized heuristics,  GRASP,  biased random-key genetic algorithm,   automatic tuning},
	att_techdoc={true},
	att_techdoc_key={TD:101106},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101106_DS1_2013-02-13T22:29:03.323Z.pdf},
	author={Mauricio Resende and L.F. Morán-Mirabal, Inst. Tec. Monterrey and José Luis González-Velarde, Inst. Tec. Monterrey},
	institution={{Lecture Notes in Computer Science}},
	month={May},
	title={{Automatic tuning of GRASP with evolutionary path-relinking}},
	year=2013,
}