The ideas of local search and random walks have been used successfully in several recent satisficing planners. Random Walk-Driven Local Search (RW-LS) is a strong new addition to this family of planning algorithms. The method uses a greedy best-first search driven by a combination of random walks and direct node evaluation. In this way, RW-LS balances between exploration and exploitation. The a...

We study maximal clades in random phylogenetic trees with the Yule–Harding model or, equivalently, in binary search trees. We use probabilistic methods to reprove and extend earlier results on moment asymptotics and asymptotic normality. In particular, we give an explanation of the curious phenomenon observed by Drmota, Fuchs and Lee (2014) that asymptotic normality holds, but one should normal...

We study a general framework for decentralized search in random graphs. Our main focus is on deterministic memoryless search algorithms that use only local information to reach their destination in a bounded number of steps in expectation. This class includes (with small modifications) the search algorithms used in Kleinberg’s pioneering work on long-range percolation graphs and hierarchical ne...

Using numerical simulation, we measured the performance of several potential quantum algorithms, based on quantum random walks, to solve Boolean satisfiability (SAT) problems. We develop the fundamentals of quantum computing and the theory of classical algorithms to indicate how these algorithms could be implemented. We also discuss the development of quantum random walks and the principles tha...

We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system signiicantly improves search as problem complexity is increased. Since the pure GP approach does not scale well with problem complexity, a natural question is which of the two components is actually contributing to the search pro...

Quantum random walks on graphs have been shown to display many interesting properties, including exponentially fast hitting times when compared with their classical counterparts. However, it is still unclear how to use these novel properties to gain an algorithmic speed-up over classical algorithms. In this paper, we present a quantum search algorithm based on the quantum random walk architectu...

Josh Singer1, Ian P. Gent2 and Alan Smaill1 Abstract. Random 2+p-SAT interpolates between the polynomialtime problem Random 2-SAT when p = 0 and the NP-complete problem Random 3-SAT when p = 1. At some value p = p0 0:41, a dramatic change in the structural nature of instances is predicted by statistical mechanics methods. This is reflected by a change in the typical cost scaling for a complete ...

We revisit a problem introduced by Bharat and Broder almost a decade ago: how to sample random pages from the corpus of documents indexed by a search engine, using only the search engine’s public interface? Such a primitive is particularly useful in creating objective benchmarks for search engines. The technique of Bharat and Broder suffers from a well-recorded bias: it favors long documents. I...

Search engines have primarily focused on presenting the most relevant pages to the user quickly. A less well explored aspect of improving the search experience is to remove or group all near-duplicate documents in the results presented to the user. In this paper, we apply a Bloom filter based similarity detection technique to address this issue by refining the search results presented to the us...