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Multiobjective optimization refers to optimization of multiple conflicting objective functions simultaneously. Clustering problem is often formulated as a multiobjective optimization problem where multiple cluster quality measures are simultaneously optimized and Pareto based approaches are popular in solving that Pareto based approaches yield a set of solutions known as Pareto front where all the...
Test data generation is the process of generating a set of input data for testing software. High quality test data helps to improve the error finding ability of software in every stages of the development life cycle. The most critical activity in software testing is the generation of test data. In existing approach, coverage based testing techniques like statement coverage, branch coverage are applied...
This paper describes the implementation of an Android application, called FuX, that can continuously play a stream of newly generated fifth species counterpoint. A variable neighborhood search algorithm is implemented in order to generate the music. This algorithm is a modification of an algorithm developed previously by the authors to generate musical fragments of a pre-specified length [28]. The...
In this paper, stochastic version of p-hub covering center problem (we call it Sp-HCCP) has been presented that optimizes the location of the hubs and allocation of non-hub nodes to hub nodes. The goal of our model is to maximize the minimum service-level that can achieved for a given maximum path length (delivery time on the path). We have formulated this problem using the chance constraints with...
The university timetabling problem deals with scheduling courses and determining the lecturer and the location of each course in a semester. This problem belongs to NP-hard problems, which means that solving the problem requires smart algorithms. In this paper, we propose a linear formulation of the problem which has been implemented using the GLPG modeling language.
Multiclass classification problems arise naturally in many tasks in computer vision; typical examples include image segmentation and letter recognition. These are among some of the most challenging and important tasks in the area and solutions to them are eagerly sought after. Genetic programming (GP) is a powerful and flexible machine learning technique that has been successfully applied to many...
Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described and new recombination operators for GP related to slp's are introduced. Experiments have been performed on...
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