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In this paper, we present a new algorithm for the Depth first searchtraversing based on Hashing. The data in the nodes are stored in a hash table and corresponding identifier or key is stored in the tree. Thus, whatever might be the data; the entries in the tree will be only integral numbers. This pre processing mark existence and location of node before searching, this causes to faster searched results. Experimental results show that proposed algorithm is simpler and faster of traversing the graph that are repeatedly queried for different goals or paths. Implementation is carried out in Java, and compared with standard DFS and BFS. [ABSTRACT FROM AUTHOR]

This paper introduces a new model checking algorithm that searches for non-progress cycles, used mainly to check for livelocks. The algorithm performs an incremental depth-first search, i.e., it searches through the graph incrementally deeper. It simultaneously constructs the state space and searches for non-progress cycles. The algorithm is expected to be more efficient than the method the model checker SPIN currently uses, and finds shortest (w.r.t. progress) counterexamples. Its only downside is the need for a subsequent reachability depth-first search (which is not the bottleneck) for constructing a full counterexample. The new algorithm is better combinable with partial order reduction than SPIN΄s method. [ABSTRACT FROM AUTHOR]

Nondeterministic conformational search techniques, such as Genetic Algorithms (GAs) are promising for solving protein structure prediction (PSP) problem. The crossover operator of a GA can underpin the formation of potential conformations by exchanging and sharing potential sub-conformations, which is promising for solving PSP. However, the usual nature of an optimum PSP conformation being compact can produce many invalid conformations (by having non-self-avoiding-walk) using crossover. While a crossover-based converging conformation suffers from limited pathways, combining it with depth-first search (DFS) can partially reveal potential pathways. DFS generates random conformations increasingly quickly with increasing length of the protein sequences compared to random-move-only-based conformation generation. Random conformations are frequently applied for maintaining diversity as well as for initialization in many GA variations. [ABSTRACT FROM AUTHOR]