The aim of the article is to present a mathematical definition of the object model, that is known in computer science as TreeList and to show application of this model for design evolutionary algorithm, that purpose is to generate structures based on this object. The first chapter introduces the reader to the problem of presenting data using the TreeList object. The second chapter describes the problem of testing data structures based on TreeList. The third one shows a mathematical model of the object TreeList and the parameters, used in determining the utility of structures created through this model and in evolutionary strategy, that generates these structures for testing purposes. The last chapter provides a brief summary and plans for future research related to the algorithm presented in the article.
Processor Automatic Functional Test Generation based on EvolutionaryStrategies, In: Proceedings of the 33 rd International Conference on Information Technology Interfaces ITI (V. Luzar-Stiffler, ed.), UCC Zagreb & IEEE CP, Cavtat/Dubrovnik, Croatia, June, 27-30, 2011, pp. 527–532.
 HUDEC, J. : Processor Functional Test Generation — Some Results with using of Genetic Algorithms, In: Proceedings of the 2 nd Eastern European Regional Conference on the Engineering of Computer Based Systems (ECBS-EERC 2011), Bratislava, Slovakia, Sep, 5–6, 2011 (V. Vranić, ed
Marcin Woźniak, Wojciech M. Kempa, Marcin Gabryel and Robert K. Nowicki
In this paper, application of an evolutionary strategy to positioning a GI/M/1/N-type finite-buffer queueing system with exhaustive service and a single vacation policy is presented. The examined object is modeled by a conditional joint transform of the first busy period, the first idle time and the number of packets completely served during the first busy period. A mathematical model is defined recursively by means of input distributions. In the paper, an analytical study and numerical experiments are presented. A cost optimization problem is solved using an evolutionary strategy for a class of queueing systems described by exponential and Erlang distributions.
The aim of the article is to highlight the advantages that can be obtained through the use of evolutionary strategy in software testing, specifically in the process of test data generation. The first chapter introduces the reader to the topic of the article. Presents information of the problem of software quality, test data fitness and quality criteria. The second chapter provides an overview of the publication in which is described the test data generation problem by using evolutionary strategies. In this chapter there are presented, different approaches to address the optimization problem of test data selection. The third chapter sets out the advantages which in the opinion of the author result from the application of evolutionary strategy in the process of test data generation. In this section have been drawn conclusions from the article, from books listed in the bibliography. The author of the article presents advantages of evolutionary strategy too as a person, which tests a software in practise. The last chapter in addition to summaries and conclusions, proposes the author to suggest in which issues related to testing could be used evolutionary strategies.
The article presents the use of an evolutionary algorithm for determining the shape of the guy rope sag of a steel smokestack. The author excludes the analysis of the operation of the rope, and discusses only the problem of determining parameters of the function of the adaption of the rope sag curve into empirical data, obtained by the geodetic method. The estimation of parameters of the curve and the characteristics of the accuracy of its adaption into experimental data were carried out by means of an evolutionary algorithm with the use of an evolutionary strategy (μ+λ). The correctness of the strategy presented in the paper, as an instrument for searching for a global minimum of a criterion function, has been presented using as an example the minimisation of a certain two dimensional function and the estimation of parameters of an ordinary and orthogonal regression function. Previous theoretical analyses have also been used for determining parameters of the guy rope sag of a steel smokestack, which is measured periodically. In addition approximate values of the pull forces in the guy ropes have been calculated.
Rogério Parentoni Martins, Rosana Tidon and José Alexandre Felizola Diniz-Filho
In this article, we discuss some ecological-evolutionary strategies that allow synchronization of organisms, resources, and conditions. Survival and reproduction require synchronization of life cycles of organisms with favourable environmental and ecological features and conditions. This interactive synchronization can occur directly, through pairwise or diffuse co-evolution, or indirectly, for example, as a result of actions of ecosystem engineers and facilitator species. Observations of specific interactions, especially those which have coevolved, may give the false impression that evolution results in optimal genotypes or phenotypes. However, some phenotypes may arise under evolutionary constraints, such as simultaneous evolution of multiple traits, lack of a chain of fit transitional forms leading to an optimal phenotype, or by limits inherent in the process of selection, set by the number of selective deaths and by interference between linked variants. Although there are no optimal phenotypes, optimization models applied to particular species may be useful for a better understanding of the nature of adaptations. The evolution of adaptive strategies results in variable life histories. These strategies can minimize adverse impacts on the fitness of extreme or severe environmental conditions on survival and reproduction, and may include reproductive strategies such as semelparity and iteroparity, or morphological, physiological, or behavioural traits such as diapause, seasonal polyphenism, migration, or bet-hedging. However, natural selection cannot indefinitely maintain intra-population variation, and lack of variation can ultimately extinguish populations.
Petra S. Yehnjong, Michael S. Zavada and Chris Liu
Soil seed banks are important to the maintenance and restoration of floras. Extant seed banks exhibit unique characteristics with regard to the distribution of seed size and seed density. Seeds were recovered from the Upper Pennsylvanian Wise Formation in southwest Virginia. Structurally preserved seeds were also examined from coal balls of the Pennsylvanian Pottsville and Allegheny Groups, Ohio. The size distribution of the seeds from the Wise Formation is similar to that of structurally preserved seeds of the Upper Pennsylvanian Pottsville and Allegheny Group coal balls. In contrast, the seed size distributions in extant wetland, grassland, woodland and forest habitats are significantly narrower than that of seeds from the Pennsylvanian seed banks. Larger seeds are less dependent on light for germination, and aid in seedling establishment more than smaller seeds, especially in dense stable forests where disturbance events are rare. Large seed size may contribute to increased seed longevity, which reduces the effect of environmental variability on seed germination and development. The significantly larger size of the Palaeozoic seeds may have imparted an advantage for seedling establishment in the dense Palaeozoic forests. The preponderance of large seeds may be a result of the absence of large seed predators (e.g. herbivorous tetrapods), and may have been an evolutionary strategy to minimize damage to the embryo from a predator population dominated by small invertebrates with chewing or sucking mouthparts. The estimated seed density of 192 seeds/m2 in the Palaeozoic seed bank falls within the range of modern seed banks, but at the lower end of modern seed bank densities in a variety of habitats.
Soňa Duchovičová, Barbora Zahradníková and Peter Schreiber
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2. FROWD, Ch., et al. 2004. The process of facial composite production. Available on: <http://www.evofit.co.uk/wp-content/uploads/2014/03/Frowd-et-al.-2004.-The
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