2010 2nd International Conference on Information Engineering and Computer Science 2010
DOI: 10.1109/iciecs.2010.5677757
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K-Noncrossing Trees and K-Proper Trees

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Cited by 4 publications
(3 citation statements)
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“…that make the optimization problems difficult to handle and can lead an optimizer to a suboptimal region in the search space. Simulated annealing, Monte-Carlo sampling, Stochastic tunneling, Parallel tempering, Stochastic Hill Climbing, PSO, GA, Evolution Strategies, Memetic Algorithms, Differential Evolution are some examples of stochastic algorithms [9,10,11,12,13,2,3,4,5,14,6,7,8,15]. Our focus in this review has been mainly on pure evolutionary computing techniques and their hybrids in which power of several methods are combined to produce a superior algorithm for global search.…”
Section: Types Of Optimization Algorithmsmentioning
confidence: 99%
“…that make the optimization problems difficult to handle and can lead an optimizer to a suboptimal region in the search space. Simulated annealing, Monte-Carlo sampling, Stochastic tunneling, Parallel tempering, Stochastic Hill Climbing, PSO, GA, Evolution Strategies, Memetic Algorithms, Differential Evolution are some examples of stochastic algorithms [9,10,11,12,13,2,3,4,5,14,6,7,8,15]. Our focus in this review has been mainly on pure evolutionary computing techniques and their hybrids in which power of several methods are combined to produce a superior algorithm for global search.…”
Section: Types Of Optimization Algorithmsmentioning
confidence: 99%
“…The special case k = 2 was considered in [8], where a bijection between 2-noncrossing trees with a root labelled 2 and 5-ary trees was constructed. The more general case was studied in [6], see also [5]. In analogy to Theorem 1.1, we will also be counting k-noncrossing trees by the number of vertices of each label.…”
Section: Introductionmentioning
confidence: 99%
“…These trees were generalized by Pang and Lv in [7] to k-noncrossing trees. A k-noncrossing tree is a noncrossing tree where each node receives a label in {1, 2, .…”
Section: Introductionmentioning
confidence: 99%