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In addition, to improve the search efficiency, a hybridized initialization is employed to generate an initial population consisting of both heuristic and random individuals.
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We compare inconsistency measure with other measures and study different search strategies such as exhaustive, complete, heuristic and random search, that can be applied to this measure.
Thirdly, a resultful initialization scheme combining heuristic and random strategies is introduced to make the initial harmony memory (HM) occur with certain quality and diversity.
An efficient genetic algorithm and an efficient greedy-penalty heuristic are, respectively, used to solve the two problems and we show that these perform significantly better than simple heuristic and random approaches.
In fact, Kudo and Sklansky proved that genetic algorithms have a higher probability of finding better solutions to naive feature selection problems than other complete, heuristic and random algorithms [ 16].
Algorithm 1, Heuristic 1 and random Heuristic [8] are compared with each other.
It is also beneficial to mix heuristics based and random distances, with s = 0.5 being fairly good in practice.
The confidence values for each clade in MP and ML were assessed by bootstrap [ 81] with 103 pseudoreplicates, heuristic searching, and random-addition of sequences.
Bootstrap support for clades found by PAUP* were calculated from 1000 replicates of heuristic search using TBR and random addition.
Also shown in the figure are the power consumptions of deterministic and random heuristics.
This number is chosen in such a way that it provides a fair comparison between VNS and random subset heuristics.
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