@Article{AndRib02a,
         author = "A. Andreatta and C.C. Ribeiro",
         title  = "Heuristics for the phylogeny problem",
        journal = "Journal of Heuristics",
        volume = "8",
        pages = "429--447", 
        year = "2002",
        annote   =     "A phylogeny is an evolutionary tree that relates 
			taxonomic units, based on their similarity over 
			a set of characters. 
			To solve the phylogeny problem means to find 
			a phylogeny with the minimum number of evolutionary 
			steps, i.e. appying the so called parsimony criterion. 
			In this paper, several different heuristic approaches 
			are studied and tested, including a {GRASP}. 
			Three different neighborhood structures have been 
			investigated: the nearest neighborhood interchange, 
			the single step neighborhood, and the subtree
                        pruning and regrafting.  
			The first one is defined by permutations of subtrees 
			around internal branches, while the single step 
			neighborhood removes at each iteration a taxon 
			(i.e. a leaf) and puts 
			it back into another branch of the tree. 
			In the subtree pruning and regrafting, a neighbor 
			is obtained by eliminating an internal node and 
			its three adjacent branches. 
			Then, two pending nodes are joined by a new branch 
			and the still pending subtree is reconnected by its 
			pending node to a branch of the other subtree.",
}

@techreport{AndRas03a,
        author = "M. Andronescu and B. Rastegari",
        title = {{Motif-{GRASP} and {Motif-ILS}: {T}wo new stochastic local
	         search algorithms for motif finding}},
        institution = "Computer Science Department, University of British
	               Columbia",
        address = "Vancouver, Canada",
	annote = {A motif is a conserved pattern thought to exist in several 
		biosequences such as DNA, RNA, and proteins. 
		Given $N$ biosequences $S_i$, $i=1,2,\ldots,N$ with length 
		$n_i$ and a number $L$, the problem of motif finding 
		consists in finding a sequence $M_i$ of length $L$ for each 
		biosequence such that their similarity grade is maximized. 
		A candidate solution is represented as a set 
		$a_1,a_2,\ldots,a_N$, where $a_k\in [1,n_k-L+1]$, for 
		each $k\in [1,N]$. 
		All candidate solutions correspond to all possible 
		combinations of $a_i$ assignment. 
		Several greedy functions are proposed based on the 
		a weight defined on the starting point of the motif. 
		One of such greedy function is the ratio between the 
		probability of generating a certain subsequence $x$ from the 
		current motif and the probability of generating $x$ from the 
		background. 
		The neighborhood structure used in the local search procedure 
		is a $1$-exchange.},
        year = "2003"
}

@phdthesis{Bro00a,
        author = "D.C. Brown",
        title = "Algorithmic methods in genetic mapping",
	school = "Cornell University",
        address = "Ithaca, NY, USA",
	annote = "This thesis is a survey of existing methods for 
		genetic mapping problems and proposed several new 
		algorithms. 
		A {GRASP} has been also investigated. 
		The greedy function is defined on bin length, while the 
		local search first removes from the sample those population 
		members that do not affect on the objective function value. 
		Then, it tries to greedily augment the sample until no 
		member can been removed without increasing the minimum 
		bin size",
        year = "2000",
}

@Article{BroVisTan00a,
        author = "D.G. Brown and T.J. Vision and S.D. Tanksley",
        title = {{Selecting mapping: {A} discrete optimization approach to 
		select a population subset for use in a high-density 
		genetic mapping project}},
        journal = "Genetics",
        volume = "155",
        pages = "407--420",
        year = "2000",
        annote   =     "The authors propose a {GRASP} for selecting 
			a population subset for use in a high-density
                	genetic mapping project. 
			At each iteration of the construction phase, 
			they add to the partial solution one among the 
			$r$ unchosen population members which most 
			improve the objective function value. 
			The authors investigated very small sized RCL 
			(i.e. $r=3$ and $r=5$). 
			The implemented local search removes from the 
			current solution some members and greedily includes 
			other members, until no further improving exchange 
			can be done.",
}

@techreport{FriHorProStrStr03a,
        title = {The footprint sorting problem},
	author = {C. Fried and W. Hordijk and S.J. Prohaska and C.R. Stradler
	          and P.F. Stradler},
	institution = {Bioinformatics, Department of Computer Science,
	               University of Leipzig, Germany},
	annote = {Phylogenetic footprints are short pieces of no-coding 
		DNA sequence in genes that are conserved between evolutionary 
		distant species. 
		The authors of this paper show that solving the footprint 
		sorting problem requires the solution of a minimum weight 
		vertex feedback set problem. 
		For solving the latter, they use the {GRASP} provided 
		in Festa et al. (2001).},
	year = {2003}
}

@inproceedings{IorTriLiaWal99a,
	author = "V.I. Iorvik and E. Triantaphyllou and T.W. Liao and 
		S.M. Waly",
	title = "Predicting muscle fatique via electromyography: {A} 
		comparative study",
	booktitle = "Proceedings of the 25th International Conference 
	       	    on Computers and Industrial Engineering",
	city = "New Orleans, LA, USA",
	month = "March",
	year = "1999",
	annote = "The authors present a comparison of some 
		state-of-the-art AI predictive 
		and statistical techniques, including a {GRASP}.",
	pages = "277--280"
}

@techreport{KraPelRusTer98a,
	 author = "N. Krasnogor and D.A. Pelta and W. Russo and
                   G. Terrazas",
	 title = "A {GRASP} approach to the protein structure
                  prediction problem",
	 institution = "LIFIA Lab, University of La Plata",
	 address = "La Plata, Argentina",
	 year = "1998",
	annote = 	"This paper discusses the
                	applicability of a {GRASP} for solving
                	a special protein folding problem, an important problem
                	in computational biology. The goal is to
                	predict from the molecular sequence of a given protein
                	its particular 3D structure."
}

@incollection{ReyDicRobWesIglBoeRay03a,
        author = {A.P. Reynolds and J.L. Dicks and I.N. Roberts and
	          J.J. Wesselink and B. {de la Iglesia} and V. Robert and 
                  T. Boekhout and V.J. Rayward-Smith},
        title = {Algorithms for identification key generation and optimization
	         with application to yeast identification},
        booktitle = {Applications of Evolutionary Computing},
	series = {Lecture Notes in Computer Science},
        publisher = "Springer-Verlag",
	volume = {2611},
	pages = {107--118},
	annote = {For the automated creation of low cost identification 
		keys, several algorithms are described in this 
		paper. 
		One of them applies the greedy randomized strategy of 
		the {GRASP} framework.},
	year = {2003}
}

@techreport{RibVia03a,
        author = "C.C. Ribeiro and D.S. Vianna",
        title = {A {GRASP}/{VND} heuristic for the phylogeny problem using
	         a new neighborhood structure},
        institution = {Department of Computer Science, Catholic U. of Rio
	               de Janeiro},
        address = "Rio de Janeiro, Brazil",
	annote = {The phylogeny problem consists in finding a phylogeny 
		with the minimum number of evolutionary steps, where a 
		phylogeny is a tree that relates taxonomic units based 
		on their similarity over a set of characters. 
		The authors propose a hybridization of {GRASP} and VND. 
		The greedy criterion is based on insertions of new taxons
                into a branch of the current partial solution.
		The local search phase uses a VND strategy and a new 
		neighborhood structure, called $2$-SPR and based on 
		the iterative removing of branches on the cuurent solution.},
        year = "2003"
}