An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied to solve hard real software engineering problems. However, to choose and design a MOEA is considered a difficult task, since there are several parameters and components to be configured. These aspects directly impact the generated solutions and the performance of MOEAs. In this sense, this paper proposes an approach for the automatic generation of MOEAs applied to the Integration and Test Order (ITO) problem. Such a problem refers to the generation of optimal sequences of units for integration testing. The approach includes a set of parameters and components of different MOEAs, and is implemented with two design algorithms: Grammatical Evolution (GE) and Iterated Racing (irace). Evaluation results are presented, comparing the MOEAs generated by both design algorithms. Furthermore, the generated MOEAs are compared to two well-known MOEAs used in the literature to solve the ITO problem. Results show that the MOEAs generated with GE and irace perform similarly, and both outperform traditional MOEAs. The approach can reduce efforts spent to design and configure MOEAs, and serves as basis for implementing solutions to other software engineering problems

Grammatical Evolution for the Multi-Objective Integration and Test Order Problem

Search techniques have been successfully applied for solving different software testing problems. However, choosing, implementing and configuring a search technique can be hard tasks. To reduce efforts spent in such tasks, this paper presents an offline hyper-heuristic named GEMOITO, based on Grammatical Evolution (GE). The goal is to automatically generate a Multi-Objective Evolutionary Algorithm (MOEA) to solve the Integration and Test Order (ITO) problem. The MOEAs are distinguished by components and parameters values, described by a grammar. The proposed hyper-heuristic is compared to conventional MOEAs and to a selection hyper-heuristic used in related work. Results show that GEMOITO can generate MOEAs that are statistically better or equivalent to the compared algorithms

Self-organizing map based adaptive sampling

We propose a new adaptive sampling method that uses Self-Organizing Maps (SOM). In SOM, densely sampled regions in the input space is represented by a larger area on the map than that of sparsely sampled regions. We use this property to progressively tune-in on the interesting region of the design space. The method does not rely on parameterized distribution, and can sample from multi-modal and non-convex distributions. In this paper, we minimize several mathematical test functions. We also show its performance in inequality-constrained objective satisfaction problem, in which the objective is to seek diversity in solutions satisfying certain upper-bound constraint in the minimized objective. A new merit function and a measure of space-filling quality were proposed for this purpose

Optimization of a perfect absorber multilayer structure by genetic algorithms

Background: An increasing interest has been recently grown in the development of nearly perfect absorber materials for solar energy collectors and more in general for all the thermophotovoltaic applications. Methods: Wide angle and broadband perfect absorbers with compact multilayer structures made of a sequence of ITO and TiN layers are here studied to develop new devices for solar thermal energy harvesting. Genetic Algorithms are introduced for searching the optimal thicknesses of the layers so to design a perfect broadband absorber in the visible range, for a wide range of angles of incidence from 0° to 50°, and for both polarizations. Results: Genetic Algorithms allow to design several optimized structures with 6, 8, and 10 layers reaching a very high average absorbance of 97%, 99% and 99.5% respectively together with a low hemispherical total emissivity (<20%) from 200 °C till 400 °C. Conclusions: The proposed multilayer structures use materials with high thermal stability, and high melting temperature, can be fabricated with simple thin film deposition techniques, appearing to have very promising applications in solar thermal energy harvesting

Real-time optical micro-manipulation using optimized holograms generated on the GPU

Holographic optical tweezers allow the three dimensional, dynamic, multipoint manipulation of micron sized dielectric objects. Exploiting the massive parallel architecture of modern GPUs we can generate highly optimized holograms at video frame rate allowing the interactive micro-manipulation of 3D structures.Comment: 13 pages, 8 figure