Комбинирование Алгоритмов Оптимизации Муравьиными Колониями и Н-Метода

* Исследования проведены при частичной поддержке INTAS (проект 06-1000017-8909)

Codon-precise, synthetic, antibody fragment libraries built using automated hexamer codon additions and validated through next generation sequencing

We have previously described ProxiMAX, a technology that enables the fabrication of precise, combinatorial gene libraries via codon-by-codon saturation mutagenesis. ProxiMAX was originally performed using manual, enzymatic transfer of codons via blunt-end ligation. Here we present Colibra™: an automated, proprietary version of ProxiMAX used specifically for antibody library generation, in which double-codon hexamers are transferred during the saturation cycling process. The reduction in process complexity, resulting library quality and an unprecedented saturation of up to 24 contiguous codons are described. Utility of the method is demonstrated via fabrication of complementarity determining regions (CDR) in antibody fragment libraries and next generation sequencing (NGS) analysis of their quality and diversity.

Использование Алгоритма Ускоренного Вероятностного Моделирования в Схеме Табу-Поиска

Рассматривается метаэвристический метод комбинаторной оптимизации, основанный на использовании алгоритмов табу-поиска и ускоренного вероятностного моделирования. Излагается общая вычислительная схема предложенного метода, названного алгоритмом GS-tabu. Приведены результаты серии вычислительных экспериментов по решению известных задач коммивояжера и квадратичных задач о назначении.

О Классификации Приближенных Методов Комбинаторной Оптимизации

В работе предлагается классификация приближенных методов комбинаторной оптимизации, которая обобщает и дополняет существующие подходы.

Gene therapy strategies for the treatment of spinal cord injury

Spinal cord injury is a complex pathology often resulting in functional impairment and paralysis. Gene therapy has emerged as a possible solution to the problems of limited neural tissue regeneration through the administration of factors promoting axonal growth, while also offering long-term local delivery of therapeutic molecules at the injury site. Of note, gene therapy is our response to the requirements of neural and glial cells following spinal cord injury, providing, in a time-dependent manner, growth substances for axonal regeneration and eliminating axonal growth inhibitors. Herein, we explore different gene therapy strategies, including targeting gene expression to modulate the presence of neurotrophic growth or survival factors and increase neural tissue plasticity. Special attention is given to describing advances in viral and non-viral gene delivery systems, as well as the available routes of gene delivery. Finally, we discuss the future of combinatorial gene therapies and give consideration to the implementation of gene therapy in humans. © 2014 Future Science Ltd.

A Metaheuristic Approach to Solving the Generalized Vertex Cover Problem

AMS Subj. Classification: 90C27, 05C85, 90C59

Solving the Task Assignment Problem with a Variable Neighborhood Search

In this paper a variable neighborhood search (VNS) approach for the task assignment problem (TAP) is considered. An appropriate neighborhood scheme along with a shaking operator and local search procedure are constructed specifically for this problem. The computational results are presented for the instances from the literature, and compared to optimal solutions obtained by the CPLEX solver and heuristic solutions generated by the genetic algorithm. It can be seen that the proposed VNS approach reaches all optimal solutions in a quite short amount of computational time.

An Electromagnetism Metaheuristic for the Uncapacitated Multiple Allocation Hub Location Problem

In this article, the results achieved by applying an electromagnetism (EM) inspired metaheuristic to the uncapacitated multiple allocation hub location problem (UMAHLP) are discussed. An appropriate objective function which natively conform with the problem, 1-swap local search and scaling technique conduce to good overall performance.Computational tests demonstrate the reliability of this method, since the EM-inspired metaheuristic reaches all optimal/best known solutions for UMAHLP, except one, in a reasonable time.

A Method for Classification of Doubly Resolvable Designs and Its Application

This article presents the principal results of the Ph.D. thesis Investigation and classification of doubly resolvable designs by Stela Zhelezova (Institute of Mathematics and Informatics, BAS), successfully defended at the Specialized Academic Council for Informatics and Mathematical Modeling on 22 February 2010.

Self-Avoiding Walks in the Plane

Румен Руменов Данговски, Калина Христова Петрова - Разглеждаме броя на несамопресичащите се разходки с фиксирана дължина върху целочислената решетка. Завършваме анализа върху случая за лента, с дължина едно. Чрез комбинаторни аргументи получаваме точна формула за броя на разходките върху лента, ограничена отляво и отдясно. Формулата я изследваме и асимптотично.

Evolution of Sets Systems and Homotopy Groups of Spheres

Симеон Т. Стефанов, Велика И. Драгиева - В работата е изследвана еволюцията на системи от множества върху n-мерната евклидова сфера S^n. Установена е връзката на такива системи с хомотопичните групи на сферите. Получени са някои комбинаторни приложения за многостени.

A Variable Neighborhood Search Approach for Solving the Maximum Set Splitting Problem

This paper presents a Variable neighbourhood search (VNS) approach for solving the Maximum Set Splitting Problem (MSSP). The algorithm forms a system of neighborhoods based on changing the component for an increasing number of elements. An efficient local search procedure swaps the components of pairs of elements and yields a relatively short running time. Numerical experiments are performed on the instances known in the literature: minimum hitting set and Steiner triple systems. Computational results show that the proposed VNS achieves all optimal or best known solutions in short times. The experiments indicate that the VNS compares favorably with other methods previously used for solving the MSSP. ACM Computing Classification System (1998): I.2.8.

Orthogonal Resolutions and Latin Squares

Resolutions which are orthogonal to at least one other resolution (RORs) and sets of m mutually orthogonal resolutions (m-MORs) of 2-(v, k, λ) designs are considered. A dependence of the number of nonisomorphic RORs and m-MORs of multiple designs on the number of inequivalent sets of v/k − 1 mutually orthogonal latin squares (MOLS) of size m is obtained. ACM Computing Classification System (1998): G.2.1.

Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach

Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. Also, the information represented by current process plan models for three-axis machining is not sufficient for five-axis machining owing to the two extra degrees of freedom and the difficulty of set-up planning. In this paper, a representation of process plans for five-axis machining is proposed, and the complicated operation sequencing process is modelled as a combinatorial optimization problem. A modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles 'fly' intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particle movements to form a modified PSO algorithm. A case study used to verify the performance of the modified PSO algorithm shows that the developed PSO can generate satisfactory results in optimizing the process planning problem. © IMechE 2009.

Test, Teachers, Quorum (Pure Populations)

The “trial and error” method is fundamental for Master Minddecision algorithms. On the basis of Master Mind games and strategies weconsider some data mining methods for tests using students as teachers.Voting, twins, opposite, simulate and observer methods are investigated.For a pure data base these combinatorial algorithms are faster then manyAI and Master Mind methods. The complexities of these algorithms arecompared with basic combinatorial methods in AI. ACM Computing Classification System (1998): F.3.2, G.2.1, H.2.1, H.2.8, I.2.6.