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—CUDASW++ is a parallelization of the Smith-Waterman algorithm for CUDA graph-ical processing units that computes the similarity scores of a query sequence paired with each sequence in a database. The algorithm uses one of two kernel functions to compute the score between a given pair of sequences: the inter-task kernel or the intra-task kernel. We have… (More)

A new recombination operator is introduced for the Traveling Salesman Problem called <i>partition crossover</i>. Theoretical and empirical results indicate that when two local optima are recombined using partition crossover, two offspring are produced that are highly likely to also be local optima. Thus, the operator is capable of jumping or… (More)

We present a hybrid Genetic Algorithm that incorporates the Generalized Partition Crossover (GPX) operator to produce an algorithm that is competitive with the state of the art for the Traveling Salesman Problem (TSP). GPX is respectful, transmits alleles and is capable of tunneling directly to new local optima. Our results show that the Hybrid Genetic… (More)

The solution space of the travelling salesman problem under 2-opt moves has been characterized as having a big-valley structure, in which the evaluation of a tour is positively correlated to the distance of the tour from the global optimum. We examine the big-valley hypothesis more closely and show that while the big-valley structure does appear in much of… (More)

Local search methods based on explicit neighborhood enumeration require at least $O(n)$ time to identify all possible improving moves. For k-bounded pseudo-Boolean optimization problems, recent approaches have achieved $O(k^2*2^{k})$ runtime cost per move, where $n$ is the number of variables and $k$ is the number of variables per subfunction. Even though… (More)

Stochastic local search (SLS) is the dominant paradigm for incomplete SAT and MAXSAT solvers. Early studies on small 3SAT instances found that the use of " best improving " moves did not improve search compared to using an arbitrary " first improving " move. Yet SLS algorithms continue to use best improving moves. We revisit this issue by studying very… (More)

By converting the MAXSAT problem to Walsh polynomials, we can efficiently and exactly compute the hyperplane averages of fixed order <i>k</i>. We use this fact to construct initial solutions based on variable configurations that maximize the sampling of hyperplanes with good average evaluations. The Walsh coefficients can also be used to implement a… (More)

Multi-trial Lin-Kernighan-Helsgaun 2 (LKH-2) is widely considered to be the best Interated Local Search heuristic for the Traveling Salesman Problem (TSP) and has found the best-known solutions to a large number of benchmark problems. Although LKH-2 performs exceptionally well on most instances, it is known to have difficulty on clustered instances of the… (More)

Stochastic local search (SLS) is the dominant paradigm for incomplete SAT and MAXSAT solvers. Early studies on small 3SAT instances found that the use of " best improving " moves did not improve search compared to using an arbitrary " first improving " move. Yet SLS algorithms continue to use best improving moves. We revisit this issue by studying very… (More)

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