In the last years the use of Search-Based techniques has been suggested to estimate software development effort. These techniques are meta-heuristics able to find optimal or near optimal solutions to problems characterized by large space. In the context of effort estimation Search-Based approaches

This paper addresses the problem of determining the next set of releases in the course of software evolution. It formulates both ranking and selection of candidate software components as a series of feature subset selection problems to which search based software engineering can be applied

This poster paper addresses the problem of choosing sets of software components to combine in component–based software engineering. It formulates both ranking and selection problems as feature subset selection problems to which search based software engineering can be applied. We will consider

an object as a collection of disjoint sets. A practical shape representation scheme, though, should give a representation that is computationally efficient to use. Unfortunately, little work has been done for the morphological skeleton and the MSD to address efficiency. We propose a flexible search-based

algorithms are compared to randomly generated results and the Genetic Algorithm is shown to be the best of the proposed search algorithms in terms of the quality of concept binding, as measured by the fitness function. The fixed boundary Hypothesis Based Concept Assignment technique is compared to the new

Abstract The paper reports a novel approach for the problem of automatic gridding in Microarray images. Such problem often requires human intervention; therefore, the development of automated procedures is a fundamental issue for large-scale functional genomic experiments involving many microarray

and offers a search-based algorithm to assist the negotiating parts in the achievement of an agreement.

into two distinct approaches. The first is based on data completion, while the second is based on what we call maximum restricted likelihood. We investigate and compare the two approaches in the framework of EAST, a newly developed search procedure for learning LT models. 1

Abstract—We apply a search-based technique for learn-ing high-quality Bayesian networks from proteomic flow-cytometry data for a portion of the human T-cell sig-naling network. Although Bayesian network models have been learned from this data [1], [2], [3] using Markov Chain Monte Carlo (MCMC

these applications appealing to mobile users. However, in many cases the available bandwidth is limited, and its excessive usage can even be a cost when wireless connections are paid on a Kbyte transfer basis. This paper proposes an approach based on Genetic Algorithms and an environment that, based on previous