Wisdom of artificial crowds algorithm for solving

We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional

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We consider the problem of finding the unconstrained global minimum of a realvalued polynomial p(x) : R R, as well as the global minimum of p(x), in a compact set K defined by polynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear

92> 2 to mean that there exists a non-deterministic Turing machine M that can solve Q 1 using only polynomially many calls to a unit-time oracle for solving Q 2 , each call having polynomially-bounded input. In other words, if we can solve all instances of problem Q 1 by consulting

It is well known that for many NP-complete problems, such as K-Sat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P != NP). This paper shows that NP-complete problems can be summarized by at least one "order parameter", and that the hard

in manufacturing processes and computer science, and it has attracted significant atten-tion in the last few years. The main focus has been on online competitive algorithms. Surprisingly little is known on the basic offline problem. In this paper, we show that the sorting buffer problem with uniform cost is NP-hard

Considerable evidence indicates that domain specific knowledge in the form of schemes is the primary factor distinguishing experts from novices in problem-solving skill. Evidence that conventional problem-solving activity is not effective in schema acquisition is also accumulating. It is suggested

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