the robust coloring problem (rcp) is a generalization of the well-known graph coloring problem where we seek for a solution that remains valid when extra edges are added. the rcp is used in scheduling of events with possible last-minute changes and study frequency assignments of the electromagnetic spectrum. this problem has been proved as np-hard and in instances larger than 30 vertices, meta-...

this research, for the first time presents the flexible flow shop model of scheduling method for considering runway assignment and operations planning together. one of the advantages of the developed model is considering the procedures of air routes in terminal airspace and separation between consecutive aircraft which is very similar to the real world condition. there are different objective f...

We present a new approach to automatic test pattern generation for very large scale integrated sequential circuit testing. This approach is more eecient than past test generation methods, since it exploits knowledge of potential circuit defects. Our method motivates a new combinatorial optimization problem, the Tour Covering Problem. We develop heuristics to solve this optimization problem, the...

Population annealing is a Monte Carlo algorithm that marries features from simulated-annealing and parallel-tempering Monte Carlo. As such, it is ideal to overcome large energy barriers in the free-energy landscape while minimizing a Hamiltonian. Thus, population-annealing Monte Carlo can be used as a heuristic to solve combinatorial optimization problems. We illustrate the capabilities of popu...

Estimating parameters from data is a key stage of the modelling process, particularly in biological systems where many parameters need to be estimated from sparse and noisy datasets. Over the years, a variety of heuristics have been proposed to solve this complex optimization problem, with good results in some cases yet with limitations in the biological setting. In this work, we develop an alg...

This work describes a new way of employing problem-specific heuristics to improve evolutionary algorithms: the Population Training Heuristic (PTH). The PTH employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals can not be improved by such heuristics. Some new theoretical improvements not present in early algorithms are now introduce...

Both coarse-to-fine and A∗ parsing use simple grammars to guide search in complex ones. We compare the two approaches in a common, agenda-based framework, demonstrating the tradeoffs and relative strengths of each method. Overall, coarse-to-fine is much faster for moderate levels of search errors, but below a certain threshold A∗ is superior. In addition, we present the first experiments on hie...