Balanced crossover operators in Genetic Algorithms

In several combinatorial optimization problems arising in cryptography and design theory, the admissible solutions must often satisfy a balancedness constraint, such as being represented by bitstrings with a fixed number of ones. For this reason, several works in the literature tackling these optimization problems with Genetic Algorithms (GA) introduced new balanced crossover operators which ensure that the offspring has the same balancedness characteristics of the parents. However, the use of such operators has never been thoroughly motivated, except for some generic considerations about search space reduction. In this paper, we undertake a rigorous statistical investigation on the effect of balanced and unbalanced crossover operators against three optimization problems from the area of cryptography and coding theory: nonlinear balanced Boolean functions, binary Orthogonal Arrays (OA) and bent functions. In particular, we consider three different balanced crossover operators (each with two variants: \u201cleft-to-right\u201d and \u201cshuffled\u201d), two of which have never been published before, and compare their performances with classic one-point crossover. We are able to confirm that the balanced crossover operators perform better than one-point crossover. Furthermore, in two out of three crossovers, the \u201cleft-to-right\u201d version performs better than the \u201cshuffled\u201d version

Unapređenje procesiranja medicinskih digitalnih slika pomocu algoritama inteligencije rojeva

Medicina je jedna od nauka gde je omogucen znacajan napredak pojavom digitalnih slika i obrade digitalnih slika. Racunarska obrada digitalnih medicinskih slika može drasticno ubrzati proces dijagnostike pri tome otkrivajuci i najsitnije promene na tkivima koje nisu vidljive ljudskom oku. Obrada medicinskih slika ukljucuje slike generisane razlicitim izvorima kao što su rendgen, ultrazvuk, magnetna rezonanca, i snimljene razlicitim urđajima kao što su skeneri, mikroskopske slike, endoskopske kapsule i drugi. Stalni napredak u medicinskoj tehnologiji snimanja doveo je do slika visokih rezolucija, trodimenzionalnih anatomskih i fizioloških slika. Sa druge strane, ovi napreci doveli su do novih problema i izazova u procesiranju medicinskih slika. Mnogi od ovih problema predstavljaju teške optimizacione probleme za cije se rešavanje u poslednje dve decenije uspešno koriste algoritmi inspirisani prirodom, posebno algoritmi inteligencije rojeva. Da bi se ovi algoritmi primenili na probleme optimizacije u obradi medicinskih digitalnih slika, neohodno je da se posebno prilagode konkretnom problemu. Ova tema predstavlja aktivnu oblast naucnog istraživanja što se može zakljuciti na osnovu velikog broja naucnih i strucnih radova, knjiga, casopisa i konferencija koji su joj posveceni. U ovoj tezi predstavljeno je nekoliko algoritma inteligencije rojeva i njihova primena na razlicite optimizacione probleme obrade medicinskih digitalnih slika. Konkretno, algoritam slepog miša, algoritam vatrometa i algoritam svica korišceni su za registraciju slika retine, segmentaciju MRI slika mozga, detekciju krvarenja na slikama endoskopske kapsule, kompresiju slika, detekciju leukemije na mikroskopskim slikama i detekciju emfisema na CT slikama pluca. Svaki od razmatranih problema je specifican i za njihovo rešavanje prilagođeni su algoritmi inteligencije rojeva. Modifikovani i prilagođeni algoritmi inteligencije rojeva za primenu u obradi medicinskih digitalnih slika testirani su na standardnim skupovima test slika prikupljenim za razmatrane probleme. Poređenjem predloženih metoda unapređenja obrade medicinskih digitalnih slika pomocu algoritama inteligencije rojeva sa drugim savremenim algoritmima iz literature, pokazano je da su dobijeni bolji rezultati, što dovodi do zakljucka da je moguce pronaci bolje metode i tehnike za rešavanje problema optimizacije koji se pojavljuju prilikom analize i obrade medicinskih digitalnih slika prilagođavanjem i primenom algoritama inteligencije rojeva

Atlantic Salmon (Salmo salar L.) as a Marine Functional Source of Gamma-Tocopherol

Gamma tocopherol (gT) exhibits beneficial cardiovascular effects partly due to its anti-inflammatory activity. Important sources of gT are vegetable oils. However, little is known to what extent gT can be transferred into marine animal species such as Atlantic salmon by feeding. Therefore, in this study we have investigated the transfer of dietary gT into salmon. To this end, fish were fed a diet supplemented with 170 ppm gT for 16 weeks whereby alpha tocopherol levels were adjusted to 190 ppm in this and the control diet. Feeding gT-rich diets resulted in a three-fold increase in gT concentrations in the liver and fillet compared to non-gT-supplemented controls. Tissue alpha tocopherol levels were not decreased indicating no antagonistic interaction between gamma- and alpha tocopherol in salmon. The concentration of total omega 3 fatty acids slightly increased in response to dietary gT. Furthermore, dietary gT significantly decreased malondialdehyde in the fillet, determined as a biomarker of lipid peroxidation. In the liver of gT fed salmon we observed an overall down-regulation of genes involved in lipid homeostasis. Additionally, gT improved the antioxidant capacity by up-regulating Gpx4a gene expression in the pyloric caeca. We suggest that Atlantic salmon may provide a marine functional source capable of enriching gT for human consumption

Impact of nutrients and water level changes on submerged macrophytes along a temperature gradient: A pan-European mesocosm experiment

Submerged macrophytes are of key importance for the structure and functioning of shallow lakes and can be decisive for maintaining them in a clear water state. The ongoing climate change affects the macrophytes through changes in temperature and precipitation, causing variations in nutrient load, water level and light availability. To investigate how these factors jointly determine macrophyte dominance and growth, we conducted a highly standardized pan-European experiment involving the installation of mesocosms in lakes. The experimental design consisted of mesotrophic and eutrophic nutrient conditions at 1 m (shallow) and 2 m (deep) depth along a latitudinal temperature gradient with average water temperatures ranging from 14.9 to 23.9 degrees C (Sweden to Greece) and a natural drop in water levels in the warmest countries (Greece and Turkey). We determined percent plant volume inhabited (PVI) of submerged macrophytes on a monthly basis for 5 months and dry weight at the end of the experiment. Over the temperature gradient, PVI was highest in the shallow mesotrophic mesocosms followed by intermediate levels in the shallow eutrophic and deep mesotrophic mesocosms, and lowest levels in the deep eutrophic mesocosms. We identified three pathways along which water temperature likely affected PVI, exhibiting (a) a direct positive effect if light was not limiting; (b) an indirect positive effect due to an evaporation-driven water level reduction, causing a nonlinear increase in mean available light; and (c) an indirect negative effect through algal growth and, thus, high light attenuation under eutrophic conditions. We conclude that high temperatures combined with a temperature-mediated water level decrease can counterbalance the negative effects of eutrophic conditions on macrophytes by enhancing the light availability. While a water level reduction can promote macrophyte dominance, an extreme reduction will likely decrease macrophyte biomass and, consequently, their capacity to function as a carbon store and food source

Adjuvant Therapy of Nivolumab Combined With Ipilimumab Versus Nivolumab Alone in Patients With Resected Stage IIIB-D or Stage IV Melanoma (CheckMate 915)

PURPOSE Ipilimumab and nivolumab have each shown treatment benefit for high-risk resected melanoma. The phase III CheckMate 915 trial evaluated adjuvant nivolumab plus ipilimumab versus nivolumab alone in patients with resected stage IIIB-D or IV melanoma. PATIENTS AND METHODS In this randomized, double-blind, phase III trial, 1,833 patients received nivolumab 240 mg once every 2 weeks plus ipilimumab 1 mg/kg once every 6 weeks (916 patients) or nivolumab 480 mg once every 4 weeks (917 patients) for <= 1 year. After random assignment, patients were stratified by tumor programmed death ligand 1 (PD-L1) expression and stage. Dual primary end points were recurrence-free survival (RFS) in randomly assigned patients and in the tumor PD-L1 expression-level < 1% subgroup. RESULTS At a minimum follow-up of approximately 23.7 months, there was no significant difference between treatment groups for RFS in the all-randomly assigned patient population (hazard ratio, 0.92; 95% CI, 0.77 to 1.09; P = .269) or in patients with PD-L1 expression < 1% (hazard ratio, 0.91; 95% CI, 0.73 to 1.14). In all patients, 24-month RFS rates were 64.6% (combination) and 63.2% (nivolumab). Treatment-related grade 3 or 4 adverse events were reported in 32.6% of patients in the combination group and 12.8% in the nivolumab group. Treatment-related deaths were reported in 0.4% of patients in the combination group and in no nivolumab-treated patients. CONCLUSION Nivolumab 240 mg once every 2 weeks plus ipilimumab 1 mg/kg once every 6 weeks did not improve RFS versus nivolumab 480 mg once every 4 weeks in patients with stage IIIB-D or stage IV melanoma. Nivolumab showed efficacy consistent with previous adjuvant studies in a population resembling current practice using American Joint Committee on Cancer eighth edition, reaffirming nivolumab as a standard of care for melanoma adjuvant treatment

Support vector machine optimized by firefly algorithm for emphysema classification in lung tissue CT images

Digital images and digital image processing facilitated significant progress in numerous areas where medicine is an important one of them. Computer-aided detection and diagnostics systems are used to assist specialists in interpretation of medical digital images. One of the important research issues is detection and classification of the chronic obstructive pulmonary disease in lung CT images. In this paper we proposed a method for emphysema classification based on texture and intensity features. Only six different characteristics of the uniform local binary pattern and intensity histogram were used as input vector for support vector machine that was used as classifier. Feature vector was significantly reduced compared to the other state-of-the-art methods while the classification accuracy was increased. On images from standard dataset global accuracy of our proposed algorithm was 98.18% compared to 95.24% and 93.9% of two other compared algorithms

Handwritten digit recognition by support vector machine optimized by Bat algorithm

Handwritten digit recognition is an important but very hard practical problem. This is a classification problem for which support vector machines are very successfully used. Determining optimal support vector machine is another hard optimization problem that involves tuning of the soft margin and kernel function parameters. For this optimization we adjusted recent swarm intelligence bat algorithm. We intentionally used weak set of features, four histogram projections, to prove that even under unfavorable conditions our algorithm would achieve acceptable results. We tested our approach on standard MNIST benchmark datasets and compared the results with other recent approaches from literature where our proposed algorithm achieved better results i.e. higher correct classification percentage