Mark Hauschild

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Kumara Sastry48
David E. Goldberg48
Pedro Larrañaga45
Martin Pelikan43
Shumeet Baluja39
28Martin Pelikan
9José Antonio Lozano
9Roberto Santana
8Alexander Mendiburu
7Kumara Sastry

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Publication Year

2007
2015

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An introduction and survey of estimation of distribution algorithms
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)
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
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)
Using previous models to bias structural learning in the hierarchical BOA
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)
Influence of selection and replacement strategies on linkage learning in BOA
The Bayesian optimization algorithm (BOA) uses Bayesian networks to learn linkages between the decision variables of an optimization problem. This paper studies the influence of different selection and replacement methods on the accuracy of linkage learning in BOA. Results on concatenated m-k deceptive trap functions show that the model accuracy depends on… (More)
Performance of evolutionary algorithms on NK landscapes with nearest neighbor interactions and tunable overlap
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)
Intelligent bias of network structures in the hierarchical BOA
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)
Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm
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)
Introduction to Estimation of Distribution Algorithms
Estimation of distribution algorithms (EDAs) guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. However, EDAs are not only optimization techniques; besides the optimum or its approximation, EDAs provide practitioners with a series of probabilistic models that reveal a lot of information… (More)
Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions
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)