Heuristic generation via parameter tuning for online bin packing

Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long stream of items. A recent work describes an approach for solving this problem based on a ‘policy matrix’ representation in which each decision option is independently given a value and the highest value option is selected. A policy matrix can also be viewed as a heuristic with many parameters and then the search for a good policy matrix can be treated as a parameter tuning process. In this study, we show that the Irace parameter tuning algorithm produces heuristics which outperform the standard human designed heuristics for various instances of the online bin packing problem

CHAMP: Creating Heuristics via Many Parameters for online bin packing

The online bin packing problem is a well-known bin packing variant which requires immediate decisions to be made for the placement of a lengthy sequence of arriving items of various sizes one at a time into fixed capacity bins without any overflow. The overall goal is maximising the average bin fullness. We investigate a ‘policy matrix’ representation which assigns a score for each decision option independently and the option with the highest value is chosen for one dimensional online bin packing. A policy matrix might also be considered as a heuristic with many parameters, where each parameter value is a score. We hence investigate a framework which can be used for creating heuristics via many parameters. The proposed framework combines a Genetic Algorithm optimiser, which searches the space of heuristics in policy matrix form, and an online bin packing simulator, which acts as the evaluation function. The empirical results indicate the success of the proposed approach, providing the best solutions for almost all item sequence generators used during the experiments. We also present a novel fitness landscape analysis on the search space of policies. This study hence gives evidence of the potential for automated discovery by intelligent systems of powerful heuristics for online problems; reducing the need for expensive use of human expertise

Application of Different Molecular Techniques for Characterization of Catalase-Positive Cocci Isolated from Sucuk

This study was carried out for the characterization and discrimination of the indigenous Gram positive, catalase-positive cocci (GCC) population in sucuk, a traditional Turkish dry-fermented sausage. Sucuk samples, produced by the traditional method without starter culture were collected from 8 local producers in Kayseri/Turkey and a total of 116 GCC isolates were identified by using different molecular techniques. Two different molecular fingerprinting methods; namely, randomly amplified polymorphic DNA-PCR (RAPD-PCR) and repetitive extragenic palindrome-PCR (rep-PCR), were used for the clustering of isolates and identification at species level was carried out by full length sequencing of 16S rDNA. Combining the results obtained from molecular fingerprinting and 16S rDNA sequencing showed that the dominant GCC species isolated from the sucuk samples was Staphylococcus saprophyticus followed by Staphylococcus succinus and Staphylococcus equorum belonging to the Staphylococcus genus. Real-time PCR DNA melting curve analysis and high-resolution melting (HRM) analysis targeting the V1 + V3 regions of 16S rDNA were also applied for the discrimination of isolates belonging to different species. It was observed statistically different Tm values and species-specific HRM profiles for all except 2 species (S. saprophyticus and Staphylococcus xylosus) that have high 16S rDNA sequence similarity. The combination of rep-PCR and/or PCR-RAPD with 16S rRNA gene sequencing was an efficient approach for the characterization and identification of the GCC population in spontaneously fermented sucuk. On the other hand, intercalating dye assays were found to be a simple and very promising technique for the differentiation of the GCC population at species level

The prevalence of enterotoxin and antibiotic resistance genes in clinical and intestinal Bacteroides fragilis group isolates in Turkey

This study was conducted to measure the antibiotic susceptibilities, corresponding gene contents, and the enterotoxin gene bft, in 50 Bacteroides fragilis group isolates, 25 of which were clinical and 25 intestinal. The resistance rates to amoxicillin/clavulanic acid, imipenem and metronidazole were low; ampicillin and tetracyclin resistance was high; clindamycin resistance and ermF gene presence was also high. Regarding phenotypical bacterial resistance and the presence of resistance genes, there was not statistically significant difference between clinical and intestinal isolates and bft positive and negative isolates. (C) 2015 Elsevier Ltd. All rights reserved