Estimation of Distribution Algorithms (EDAs) are a quite recent topic in optimization techniques. They combine two technical disciplines of soft computing methodologies: probabilistic reasoning and evolutionary computing. Several algorithms and approaches have already been proposed by different authors, but up to now there are very few papers showing their potential and comparing them to other evolutionary computational methods and algorithms such as Genetic Algorithms (GAs). This paper focuses on the problem of inexact graph matching which is NP-hard and requires techniques to find an approximate acceptable solution. This problem arises when a non bijective correspondence is searched between two graphs. A typical instance of this problem corresponds to the case where graphs are used for structural pattern recognition in images. EDA algorithms are well suited for this type of problems.

Semantic scene classification is a challenging problem in computer vision. Special-purpose semantic object and material (e.g., sky and grass) detectors help, but are faulty in practice. In this paper, we propose a generative model of outdoor scenes based on spatial configurations of objects in the scene. Because the number of semantically-meaningful regions (for classification purposes) in the image is expected to be small, we infer exact probabilities by utilizing a brute-force approach. However, it is impractical to obtain enough training data to learn the joint distribution of the configuration space. To help overcome this problem, we propose a smoothing technique that modifies the naive uniform (Dirichlet) prior by using modelbased graph-matching techniques to populate the configuration space. The proposed technique is inspired by the backoff technique from statistical language models. We compare scene classification performance using our method with two baselines: no smoothing and smoothing with a uniform prior. Initial results on a small set of natural images show the potential of the method. Detailed exploration of the behavior of the method on this set may lead to future improvements. 1.

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This paper describes and analyzes an estimation of distribution algorithm based on dependency tree models (dtEDA), which can explicitly encode probabilistic models for permutations. dtEDA is tested on deceptive ordering problems and a number of instances of the quadratic assignment problem. The performance of dtEDA is compared to that of the standard genetic algorithm with the partially matched crossover (PMX) and the linear order crossover (LOX). In the quadratic assignment problem, the robust tabu search is also included in the comparison.

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