Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

An immunity based configuration for multilayer single featured biometric authentication algorithms

Immune systems have been used in the last years to inspire approaches for several computational problems. This paper focus on behavioural biometric authentication algorithms’ accuracy enhancement by using them more than once and with different thresholds in order to first simulate the protection provided by the skin and then look for known outside entities, like lymphocytes do. The paper describes the principles that support the application of this approach to Keystroke Dynamics, an authentication biometric technology that decides on the legitimacy of a user based on his typing pattern captured on he enters the username and/or the password and, as a proof of concept, the accuracy levels of one keystroke dynamics algorithm when applied to five legitimate users of a system both in the traditional and in the immune inspired approaches are calculated and the obtained results are compared.

Algorithms and tools for in silico design of cell factories

PhD thesis in Bioengineering

Genetic breeding on the bee Melipona scutellaris (Apidae, Meliponinae)

A selection of queens of Melipona scutellaris through the most productive colonies were carried out during eight months in an orange honeyflow. Each of the colonies was evaluated by its production, that is, the gross weight production ( pollen, brood, geopropolis and wax of each hive). With this data a coefficient of repeatability was estimated by the intraclass correlation method, obtained r = 0.835 ± 0.071. The repeatibility is very high showing that the analysed data (production) is repeatable. Selection was then carried out using the regression coefficient of each colony and the respective production gain. Using these data the colonies were divided into three groups according to the method Vencovsky and Kerr (1982): a with the colonies of highest productivity, b of least productivity, and c of intermediary productivity. Colonies with the highest production (Group a) gave their queens to those of the lowest production (Group b) after their queens were taken out and killed; while those of intermediate (Group c) stayed with the same queens during the entire experiment both before and after the selection. The modifications in weight, that is, the genetic response was (R)= 7.98 gr per day which indicated a selection gain. The estimate of the realized herdability is twice the rate of the response to selection (R) by the selection differential (S2). That is then h²R=2(R/S2) then h²R= 0.166

In silico constraint-based strain optimization methods: the quest for optimal cell factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

Geographic pattern of genetic diversity in natural populations of Rosewood (Aniba rosaeodora), in the Central Amazonia

Rosewood (Aniba rosaeodora Ducke, Lauraceae) is an Amazonian evergreen tree and a source of the purest linalool, the main component of its essential oil, which is very valuable in the international perfumery market. After decades of over-exploitation it is currently considered as threatened. We evaluated the genetic diversity and its distribution in four populations in Central Amazonia. Thirty-five reliable RAPD markers were generated, of which 32 were polymorphic (91.4%). Variation was higher within the populations (76.5%; p < 0.0001) and geographic distribution contributed to population differentiation (23.4%; p < 0.0001). The Amazon River had a small influence on gene flow (3.3%; p < 0.0001), but we identified evidence of gene flow across the river. There were significant differences in marker frequencies (p < 0.05), in agreement with the low gene flow (Nm = 2.02). The correlation between genetic distance and gene flow was - 0.95 (p = 0.06) and between geographic distance and gene flow was -0.78 (p = 0.12). There was a geographic cline of variability across an East-West axis, influenced as well by the Amazon River, suggesting the river could be a barrier to gene flow. Although threatened, these Rosewood populations retain high diversity, with the highest levels in the Manaus population, which has been protected for over 42 years in a Reserve.

Genetic diversity in rosewood saplings (Aniba rosaeodora ducke, Lauraceae): an ecological approach

This article takes an ecological approach to the genetic diversity of Rosewood (Aniba rosaeodora Ducke) in a central Amazonian terra firme forest north of Manaus. Planted Rosewood setting, under partial shaded canopy, were assessed in terms of fruiting production, frugivory, and seed dispersal. Using RAPD molecular analysis procedures, the influence of the spatial distribution of adult trees on the genetic diversity (polymorphism) of saplings was assessed with genetic samples from 34 reproductive trees and 60 saplings. The density and distribution patterns the reproductive trees did not modify the sapling"s diversity (1.86%, AMOVA). Two types of adult tree dispersion were identified; i) clumped and ii) more widely dispersed. Polymorphism (77.5%) and gene flow were high between these. Although more sapling genetic variability in areas with a higher density of mature plants was not as high as expected, density did not affect the genetic diversity of samplings, indicating a high incidence of gene flow amongst trees. In planted Rosewood population (surrounded by low disturbed forest), fruiting trees experienced a high level of removal of seeds by toucans (Rhamphastidae), about of 50%. The high gene flow found among native trees suggested that toucans, promoting seed rain at short and long distances from maternal trees, actively contribute to the maintenance of genetic diversity within wild rosewood populations.

Testing Nelder-Mead based repulsion algorithms for multiple roots of nonlinear systems via a two-level factorial design of experiments

This paper addresses the challenging task of computing multiple roots of a system of nonlinear equations. A repulsion algorithm that invokes the Nelder-Mead (N-M) local search method and uses a penalty-type merit function based on the error function, known as 'erf', is presented. In the N-M algorithm context, different strategies are proposed to enhance the quality of the solutions and improve the overall efficiency. The main goal of this paper is to use a two-level factorial design of experiments to analyze the statistical significance of the observed differences in selected performance criteria produced when testing different strategies in the N-M based repulsion algorithm. The main goal of this paper is to use a two-level factorial design of experiments to analyze the statistical significance of the observed differences in selected performance criteria produced when testing different strategies in the N-M based repulsion algorithm.

Bootstrap performance profiles in stochastic algorithms assessment

Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed.