The development of classification systems that meet the desired accuracy levels for real world-tasks applications requires a lot of expertise. Numerous challenges, like noisy feature data, suboptimal algorithms and hyperparameters, degrade the generalization performance. On the other hand, almost countless solutions have been developed, e.g. feature selection, feature preprocessing, automatic a...

Optimization problems attempt to find out the minimum or maximum of a certain function (usually referred to as the cost function). The cost function can either be continuous or discrete. Discrete optimization problems arise, when the variables occurring in the optimization function can take only a finite number of discrete values and also subject to constraint conditions. In continuous optimiza...

Two nonoverlapping domain decomposition algorithmsare proposed for convectiondom-inated convection-diiusion problems. In each subdomain, artiicial boundary conditions are used on the innow and outtow boundaries. If the ow is simple, each subdomain problem only needs to be solved once. If there are closed streamlines, an iterative algorithmis needed and the convergence is proved. Analysis and nu...

Traditional mathematical algorithms are incapable of solving real time engineering design problems because of its rigid procedure mainly due to discrete or random data and multi-objective functions in a problem. An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till the optimum or a satisfactory solution is found. There are two population base...

Test method facet is one of the factors which can have an influence on the test takers’ performance. The purpose of the current study was to investigate the effects of two different response types, multiple-choice cloze and multiple-choice test, on the pre-intermediate and intermediate test takers’ reading comprehension performance. To this end, 40 pre-intermediate and intermediate learners par...

In this paper a methodology for finding the maximal common subgraph of two directed graphs with parallel genetic algorithm is discussed. The method is directly applicable to the optimization of configurations of FPGA (Field Programmable Gate Array) circuits in Run-Time Reconfigurable systems. The problem of finding the maximal common subgraph is known to be NP-complete. The advantage of our app...

We consider the problem of multiple fitting of linearly parametrized curves, that arises in many computer vision problems such as road scene analysis. Data extracted from images usually contain non-Gaussian noise and outliers, which makes classical estimation methods ineffective. In this paper, we first introduce a family of robust probability density functions which appears to be well-suited t...

A hybrid genetic algorithm for multiway graph partitioning is proposed. The algorithm includes an e cient local optimization heuristic. Starting at an initial solution, the heuristic iteratively improves the solution using cyclic movements of vertices. The suggested heuristic performed well in itself and as a local optimization engine in the hybrid genetic algorithm. Combined with the framework...

We describe and evaluate a novel optimization-based off-line path planning algorithm for mobile robots based on the Counterexample-Guided Inductive Optimization (CEGIO) technique. CEGIO iteratively employs counterexamples generated from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers, in order to guide the optimization process and to ensure global optimization. Thi...