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- Mark Hauschild, Martin Pelikan
- Swarm and Evolutionary Computation
- 2011

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 metaheuristics. This paper… (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)

- Cláudio F. Lima, Martin Pelikan, David E. Goldberg, Fernando G. Lobo, Kumara Sastry, Mark Hauschild
- IEEE Congress on Evolutionary Computation
- 2007

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)

- Martin Pelikan, Mark Hauschild, Dirk Thierens
- GECCO
- 2011

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)

- Mark Hauschild, Martin Pelikan, Kumara Sastry, David E. Goldberg
- GECCO
- 2008

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)

- Mark Hauschild, Martin Pelikan, Kumara Sastry, Cláudio F. Lima
- IEEE Trans. Evolutionary Computation
- 2009

- Mark Hauschild, Martin Pelikan
- GECCO
- 2010

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)

- Martin Pelikan, Mark Hauschild, Pier Luca Lanzi
- PPSN
- 2012

An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distance-based statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous hBOA runs and using the obtained statistics to bias future… (More)

- Mark Hauschild, Martin Pelikan
- GECCO
- 2011

While different measures of problem difficulty of fitness landscapes have been proposed, recent studies have shown that many of the common ones do not closely correspond to the actual difficulty of problems when solved by evolutionary algorithms. One of the reasons for this is that most problem difficulty measures are based on neighborhood structures that… (More)

- Martin Pelikan, Mark Hauschild, Fernando G. Lobo
- Handbook of Computational Intelligence
- 2015