Multi-objective optimisation of multi-output neural trees

We propose an algorithm and a new method to tackle the classification problems. We propose a multi-output neural tree (MONT) algorithm, which is an evolutionary learning algorithm trained by the non-dominated sorting genetic algorithm (NSGA)-III. Since evolutionary learning is stochastic, a hypothesis found in the form of MONT is unique for each run of evolutionary learning, i.e., each hypothesis (tree) generated bears distinct properties compared to any other hypothesis both in topological space and parameter-space. This leads to a challenging optimisation problem where the aim is to minimise the tree-size and maximise the classification accuracy. Therefore, the Pareto-optimality concerns were met by hypervolume indicator analysis. We used nine benchmark classification learning problems to evaluate the performance of the MONT. As a result of our experiments, we obtained MONTs which are able to tackle the classification problems with high accuracy. The performance of MONT emerged better over a set of problems tackled in this study compared with a set of well-known classifiers: multilayer perceptron, reduced-error pruning tree, naive Bayes classifier, decision tree, and support vector machine. Moreover, the performances of three versions of MONT’s training using genetic programming, NSGA-II, and NSGA-III suggests that the NSGA-III gives the best Pareto-optimal solution

Adaptive search space decomposition method for pre- and post-buckling analyses of space truss structures

The paper proposes a novel adaptive search space decomposition method and a novel gradient-free optimization-based formulation for the pre- and post-buckling analyses of space truss structures. Space trusses are often employed in structural engineering to build large steel constructions, such as bridges and domes, whose structural response is characterized by large displacements. Therefore, these structures are vulnerable to progressive collapses due to local or global buckling effects, leading to sudden failures. The method proposed in this paper allows the analysis of the load-equilibrium path of truss structures to permanent and variable loading, including stable and unstable equilibrium stages and explicitly considering geometric nonlinearities. The goal of this work is to determine these equilibrium stages via optimization of the Lagrangian kinematic parameters of the system, determining the global equilibrium. However, this optimization problem is non-trivial due to the undefined parameter domain and the sensitivity and interaction among the Lagrangian parameters. Therefore, we propose to formulate this problem as a nonlinear, multimodal, unconstrained, continuous optimization problem and develop a novel adaptive search space decomposition method, which progressively and adaptively re-defines the search domain (hypersphere) to evaluate the equilibrium of the system using a gradient-free optimization algorithm. We tackle three benchmark problems and evaluate a medium-sized test representing a real structural problem in this paper. The results are compared to those available in the literature regarding displacement–load curves and deformed configurations. The accuracy and robustness of the adopted methodology show a high potential for gradient-free algorithms to analyze space truss structures

Ebola: a review on the state of the art on prevention and treatment

The aim of this paper is to highlight the current development in the research field for helping people just exposed to Ebola virus survive (treatment) and to prevent the disease when given at various times after exposure (vaccine). Concerning the treatment, recombinant anti-Ebola monoclonal antibodies and small interfering RNAs that block the expression of essential viral proteins, are the most promising way in stopping the disease when it has already reached the humans. As far as concerns the prevention field, two candidate vaccines have clinical-grade vials available for phase 1 pre-licensure clinical trials, and demonstrated to have a 100% efficacy in studies on non human primates. Well-informed communities can reduce the main ways of spread the infection, by avoiding unprotected home-based care of people who are infected and also by completely modifying traditional burial practices that are way of diffusion of the contagion

Energetski kronometabolizam u novozelandskog bijelog kunića.

The temporal organization of some physiological parameters in rabbits by evaluating the circatrigintan rhythms of some haematochemical and haematological parameters, and of rectal temperature, in New Zealand white rabbits was studied. For 30 days before the study the animals all followed the same pattern of daily activity with the natural photoperiod for that season, and were fed on hay ad libitum. Subsequently, the same animals followed another pattern of daily activity for a further 30-day period with a natural photoperiod and were fed on commercial pellets. At the end of each experimental period, blood samples were taken and rectal temperature was measured every 5 days for 30 days. Spectrophotometry in UV was used to calculate the concentration in each sample of: glucose, NEFA, triglycerides, total cholesterol, total proteins, uric acid, urea, albumin, creatinine, calcium, phosphorus and magnesium. The following values were measured on samples rendered unclottable: haematocrit, haemoglobin, RBC, MCV, WBC and VES. A periodic statistical model was used to elaborate the data, on average values of the two sets of samples. Intra-group variance was not significant. Glucose, triglycerides, RBC, Hb, PCV and rectal temperature showed a circatrigintan rhythm in both the experimental periods, but with different acrophases; creatinine, magnesium and phosphorus showed periodicity only when the subjects were fed on hay, while total proteins, urea, albumin and calcium were periodic only when they were fed with commercial pellets. The results obtained showed that the type of ration synchronizes the circatrigintan rhythm of some haematochemical and haematological parameters in the rabbit.Istražena je pojavnost određenih fizioloških osobina na osnovi prosudbe mjesečnog ritma nekih biokemijskih i hematoloških pokazatelja i rektalne temperature u novozelandskog bijelog kunića. Trideset dana su životinje bile držane pod jednakim uvjetima trajanja prirodne svjetlosti i hranjene sijenom ad libitum. U drugom tridesetdnevnom razdoblju držane su pod drugim načinom dnevnih aktivnosti s prirodnim trajanjem dnevne svjetlosti te hranjene komercijalnom peletiranom hranom. Na kraju svakog pokusnog razdoblja uzimani su uzorci krvi i mjerena temperatura svakog petog dana u tijeku 30 dana. UV-spektrofotometrijom određena je u svakom uzetom uzorku koncentracija glukoze, NEFA, triglicerida, ukupnog kolesterola, ukupnih bjelančevina, mokraćne kiseline, mokraćevine, albumina, kreatinina, kalcija, fosfora i magnezija. Određivan je hematokrit, hemoglobin, broj eritrocita, leukocita i VES. Podaci su obrađeni periodičkim statističkim modelom na razini prosječnih vrijednosti dvije skupine uzoraka. Varijanca između skupina nije bila značajna. Vrijednosti glukoze, triglicerida, hemoglobina, PVC te broj eritrocita i vrijednost rektalne temperature pokazivale su tridesetdnevni ritam u oba pokusna razdoblja, ali s različitim najvišim fazama. Vrijednosti kreatinina, magnezija i fosfora pokazivale su periodičnost samo kad su životinje bile hranjene sijenom dok su vrijednosti ukupnih bjelančevina, mokraćevine, albumina i kalcija pokazivale periodičnost kad su životinje bile hranjene peletiranom komercijalnom hranom. Dobiveni rezultati su pokazali da vrst obroka usklađuje tridesetdnevni ritam određenih biokemijskih i hematoloških pokazatelja u kunića

Protein multiple sequence alignment by hybrid bio-inspired algorithms

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the ‘weighted sum of pairs’ as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BAliBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space

OREMP: Ontology Reasoning Engine for Molecular Pathways

The information about molecular processes is shared continuously in the form of runnable pathway collections, and biomedical ontologies provide a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Here we propose a strategy that integrates these resources by means of extended ontologies built on top of a common meta-format. Information sharing, integration and discovery are the primary features provided by the system; additionally, two current field applications of the system are reported

Pareto optimality in multilayer network growth

We model the formation of multi-layer transportation networks as a multi-objective optimization process, where service providers compete for passengers, and the creation of routes is determined by a multi-objective cost function encoding a trade-off between efficiency and competition. The resulting model reproduces well real-world systems as diverse as airplane, train and bus networks, thus suggesting that such systems are indeed compatible with the proposed local optimization mechanisms. In the specific case of airline transportation systems, we show that the networks of routes operated by each company are placed very close to the theoretical Pareto front in the efficiency-competition plane, and that most of the largest carriers of a continent belong to the corresponding Pareto front. Our results shed light on the fundamental role played by multi-objective optimization principles in shaping the structure of large-scale multilayer transportation systems, and provide novel insights to service providers on the strategies for the smart selection of novel routes

Multi-Target Analysis and Design of Mitochondrial Metabolism.

Analyzing and optimizing biological models is often identified as a research priority in biomedical engineering. An important feature of a model should be the ability to find the best condition in which an organism has to be grown in order to reach specific optimal output values chosen by the researcher. In this work, we take into account a mitochondrial model analyzed with flux-balance analysis. The optimal design and assessment of these models is achieved through single- and/or multi-objective optimization techniques driven by epsilon-dominance and identifiability analysis. Our optimization algorithm searches for the values of the flux rates that optimize multiple cellular functions simultaneously. The optimization of the fluxes of the metabolic network includes not only input fluxes, but also internal fluxes. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the desired Pareto front. We find that the maximum ATP production is linked to a total consumption of NADH, and reaching the maximum amount of NADH leads to an increasing request of NADH from the external environment. Furthermore, the identifiability analysis characterizes the type and the stage of three monogenic diseases. Finally, we propose a new methodology to extend any constraint-based model using protein abundances.PL has received funding from (FP7-Health-F5-2012) under grant agreement no. 305280 (MIMOmics). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLoS via http://dx.doi.org/10.1371/journal.pone.013382

Features for Exploiting Black-Box Optimization Problem Structure.

Abell T, Malitsky Y, Tierney K. Features for Exploiting Black-Box Optimization Problem Structure. In: Nicosia G, Pardalos P, eds. Learning and Intelligent Optimization: 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers. Lecture Notes in Computer Science. Vol 7997. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013: 30-36.Black-box optimization (BBO) problems arise in numerous scientific and engineering applications and are characterized by computationally intensive objective functions, which severely limit the number of evaluations that can be performed. We present a robust set of features that analyze the fitness landscape of BBO problems and show how an algorithm portfolio approach can exploit these general, problem independent, features and outperform the utilization of any single minimization search strategy. We test our methodology on data from the GECCO Workshop on BBO Benchmarking 2012, which contains 21 state-of-the-art solvers run on 24 well-established functions