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Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. This explicit use of probablistic models in optimization offers some significant advantages over other types of meta-heuristics. This paper(More)
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum (or an accurate approximation), any EDA also provides us with a sequence of(More)
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses(More)
One of the primary advantages of estimation of distribution algorithms (EDAs) over many other stochastic optimization techniques is that they supply us with a roadmap of how they solve a problem. This roadmap consists of a sequence of probabilistic models of candidate solutions of increasing quality. The first model in this sequence would typically encode(More)
This paper presents a class of NK landscapes with nearest-neighbor interactions and tunable overlap. The considered class of NK landscapes is solvable in polynomial time using dynamic programming; this allows us to generate a large number of random problem instances with known optima. Several genetic and evolutionary algorithms are then applied to the(More)
The linkage tree genetic algorithm (LTGA) identifies linkages between problem variables using an agglomerative hierarchical clustering algorithm and linkage trees. This enables LTGA to solve many decomposable problems that are difficult with more conventional genetic algorithms. The goal of this paper is two-fold: (1) Present a thorough empirical evaluation(More)
This paper analyzes the effects of restricting probabilistic models in the hierarchical Bayesian optimization algorithm (hBOA) by defining a distance metric over variables and disallowing dependencies between variables at distances greater than a given threshold. We argue that by using prior problem-specific knowledge, it is often possible to develop a(More)
Practitioners often have some information about the problem being solved, which may be represented as a graph of dependencies or correlations between problem variables. Similar information can also be obtained automatically, for example by mining the probabilistic models obtained by EDAs or by using other methods for linkage learning. This information can(More)