Category: Business Analytics and Intelligence Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 304 Biased Randomization of Classical Heuristics INTRODUCTION In the context of combinatorial optimization problems, this chapter discusses how to random- ize classical heuristics in order to transform these deterministic procedures into more efficient proba- bilistic algorithms. This randomization process can be performed by using a uniform probability distribution or, even more interesting, by using a non-symmetric distribution. Combinatorial Optimization Problems (COPs) have posed numerous challenges to the human mind throughout the past decades. From a theo- retical perspective, they have a well-structured definition consisting of an objective function that needs to be minimized or maximized, and a series of constraints that must be satisfied. From a theoretical point of view, these problems have an interest on their own due to the mathematics involved in their modeling, analysis and solution. However, the main reason for which they have been so actively investigated is the tremendous amount of real-life applications that can be suc- cessfully modeled as a COP. Thus, for example, decision-making processes in fields such as lo- gistics, transportation, and manufacturing contain plentiful hard challenges that can be expressed as COPs (Faulin et al., 2012; Montoya et al., 2011). Accordingly, researchers from different areas –e.g. Applied Mathematics, Operations Research, Computer Science, and Artificial Intelligence– have directed their efforts to conceive techniques to model, analyze, and solve COPs. A considerable number of methods and al- gorithms for searching optimal or near-optimal solutions inside the solution space have been developed. In some small-sized problems, the solu- tion space can be exhaustively explored. For those instances, efficient exact methods can usually provide the optimal solution in a reasonable time. Unfortunately, the solution space in most COPs is exponentially astronomical. Thus, in medium- or large-size problems, the solution space is too large and finding the optimum in a reasonable amount of time is not a feasible task. An exhaustive method that checks every single candidate in the solution space would be of very little help in these cases, since it would take exponential time. Therefore, a large amount of heuristics and metaheuristics have been developed in order to obtain near-optimal solutions, in reasonable computing times, for medium- and large-size problems, some of them even considering realistic constraints. Angel A. Juan IN3-Open University of Catalonia, Spain José Cáceres-Cruz IN3-Open University of Catalonia, Spain Sergio González-Martín IN3-Open University of Catalonia, Spain Daniel Riera IN3-Open University of Catalonia, Spain Barry B. Barrios IN3-Open University of Catalonia, Spain DOI: 10.4018/978-1-4666-5202-6.ch028