In this paper we investigate the use of two evolutionary based heuristic to the bin packing problem. The intractability of this problem is a motivation for the pursuit of heuristics that produce approximate solutions. Unlike other evolutionary based heuristics used with optimization problems, ours do not use domain-speciic knowledge and has no specialized genetic operators. It uses a straightfo...

This paper presents and compares three heuristics for the combinatorial auction problem. Besides a simple greedy (SG) mechanism, two metaheuristics, a simulated annealing (SA), and a genetic algorithm (GA) approach are developed which use the combinatorial auction process to find an allocation with maximal revenue for the auctioneer. The performance of these three heuristics is evaluated in the...

Meta-learning is a method of improving results of algorithm by learning from metafeatures which describe problem instances and from results produced by various algorithms on these instances. In this project we tried to apply this idea, which was already proved to be useful in machine learning, to combinatorial optimization. We have developed a general software tool called SEAGE to extract meta-...

Stochastic programming problems appear as mathematical models for optimization problems under stochastic uncertainty. Most computational approaches for solving such models are based on approximating the underlying probability distribution by a probability measure with finite support. Since the computational complexity for solving stochastic programs gets worse when increasing the number of atom...

Generally speaking, the problem of drawing a graph G is set as a combinatorial optimization problem: producing a layout L(G) of G on a given support according to a drawing convention (e.g. layered drawing) that optimizes some measurable aesthetics (e.g. arc-crossing). Numerous criteria lead to NP-complete problems, and aesthetics often conflict with each other. Hence, various heuristics from cl...

The uniting feature of combinatorial optimization and extremal graph theory is that in both areas one should find extrema of a function defined in most cases on a finite set. While in combinatorial optimization the point is in developing efficient algorithms and heuristics for solving specified types of problems, the extremal graph theory deals with finding bounds for various graph invariants u...

Many real-world problems in the production and logistics business are NPhard even in their deterministic representation, and actually also show stochastic behaviour, where even the mathematical description of the – frequently empirical – distributions is difficult or even impossible. Therefore, an approach is acquired that enables the search for valid and reasonably good solutions under represe...

Articulo sera publicado en Springer Verlag, como capitulo del libro Data Analysis and Rationality in a Complex World.