Simulated annealing and the (1+1) EA, a simple evolutionary algorithm, are both general randomized search heuristics that optimize any objective function with probability
Abstract. In constraint satisfaction, a general rule is to tackle the hardest part of a search problem first. In this paper, we introduce a parameter (τ) that measures the constr...
The problem of finding a symmetric connectivity topology with minimum power consumption in a wireless ad-hoc network is NPhard. This work presents a new iterated local search to s...
Given a set of objects with profits (any, even negative, numbers) assigned not only to separate objects but also to pairs of them, the unconstrained binary quadratic optimization p...
Graphplan and heuristic state space planners such as HSP-R and UNPOP are currently two of the most effective approaches for solving classical planning problems. These approaches h...