2001
DOI: 10.1007/3-540-45365-2_15
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A Co-evolutionist Meta-heuristic for the Assignment of the Frequencies in Cellular Networks

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Cited by 5 publications
(4 citation statements)
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“…All the analyzed works but [27] (row 4, COSEARCH) incorporate a local search method within the evolutionary cycle of the EA (either together with the recombination and mutation operators or replacing any of them). By using the taxonomy and grammar proposed by Talbi [21] to define hybrid metaheuristics, they are LTH(EA(LS)) or Low-level Teamwork Hybrids, in which the EA is usually devoted to search diversification and the LS mainly promotes search intensification of promising areas.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…All the analyzed works but [27] (row 4, COSEARCH) incorporate a local search method within the evolutionary cycle of the EA (either together with the recombination and mutation operators or replacing any of them). By using the taxonomy and grammar proposed by Talbi [21] to define hybrid metaheuristics, they are LTH(EA(LS)) or Low-level Teamwork Hybrids, in which the EA is usually devoted to search diversification and the LS mainly promotes search intensification of promising areas.…”
Section: Related Workmentioning
confidence: 99%
“…Without doubt, the usage of greedy algorithms (or hill climbers) as LS method is the most widely used strategy (included in nine out of the seventeen analyzed works) because of its ability of easily adding specific problem domain knowledge. COSEARCH [27] is the only metaheuristic that does not follow this approach. Indeed, it uses a parallel heterogeneous search model [40] in which a local search method and two metaheuristics (EA and TS) cooperate via an adaptive memory mechanism.…”
Section: Related Workmentioning
confidence: 99%
“…The routing is found according to the shortest paths (considering the routing metric of each link) that can be found using the Dijkstra algorithm (for more details about the shortest path problem, see Stern and Bala [1]). Step 1.3 ÿnds the wavelengths of the WPs by solving the WA problem (see [15][16][17] and the references contained therein for more details on the WA problem). Since this problem is NP-hard (transformation from the graph coloring problem [5]) and that one WA problem should be solved per network state (see also Step 2.2.3), a fast greedy constructive heuristic for the graph coloring problem is used (see [14,[18][19][20] and the references contained therein for more details on the graph coloring problem and heuristics).…”
Section: Algorithm Hmentioning
confidence: 99%
“…Indeed, the complexity of the real world problems in various fields [4,5,6,7] requires new optimization tools and methods which should meet time constrains of a given problem. Meaning that a solution is needed but within or respecting a possible time interval, this is the reality of the majority of real time problems.…”
Section: Introductionmentioning
confidence: 99%