Evaluating the performance of a EuroDivisia index using artificial intelligence techniques

We compare two methods in order to predict inflation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture is evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques

This paper compares two methods to predict in°ation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture are evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.

On Solving the Maximum Betweenness Problem Using Genetic Algorithms

In this paper a genetic algorithm (GA) is applied on Maximum Betweennes Problem (MBP). The maximum of the objective function is obtained by finding a permutation which satisfies a maximal number of betweenness constraints. Every permutation considered is genetically coded with an integer representation. Standard operators are used in the GA. Instances in the experimental results are randomly generated. For smaller dimensions, optimal solutions of MBP are obtained by total enumeration. For those instances, the GA reached all optimal solutions except one. The GA also obtained results for larger instances of up to 50 elements and 1000 triples. The running time of execution and finding optimal results is quite short.

Tourism in Albarracín and Region. Local Public Action and dynamics in recent key sustainable tourism development

Spanish tourist destinations in rural areas have been established over more than two decades of implementation of various public policy instruments (mainly tourism and rural development policies). These convey complementary objectives in theory but provoke distant results in practice. The intervention of these instruments produces in the region of Sierra de Albarracín (Teruel) two types of destination whose sustainability is committed: the historical urban site of Albarracín as a consolidated cultural tourism destination based on heritage and the Sierra as a generic and incipient destination of rural tourism. It is discussed how the deployment of the local public action causes a fragmented territory in two models of management and tourism development. Cooperation is presented as a key element for the necessary rethinking of tourism development in the region.

Molecular biology of prolactin and prolactin receptor in a marine teleost: the sea bream(Sparus aurata)

Tese de dout. em Biologia, especialidade de Biologia Molecular, Unidade de Ciências e Tecnologias dos Recursos Aquáticos, Univ. do Algarve

An FPGA-based approach to multi-objective evolutionary algorithm for multi-disciplinary design optimisation

This paper investigates the field programmable gate array (FPGA) approach for multi-objective and multi-disciplinary design optimisation (MDO) problems. One class of optimisation method that has been well-studied and established for large and complex problems, such as those inherited in MDO, is multi-objective evolutionary algorithms (MOEAs). The MOEA, nondominated sorting genetic algorithm II (NSGA-II), is hardware implemented on an FPGA chip. The NSGA-II on FPGA application to multi-objective test problem suites has verified the designed implementation effectiveness. Results show that NSGA-II on FPGA is three orders of magnitude better than the PC based counterpart.

A human-simulated immune evolutionary computation approach

Premature convergence to local optimal solutions is one of the main difficulties when using evolutionary algorithms in real-world optimization problems. To prevent premature convergence and degeneration phenomenon, this paper proposes a new optimization computation approach, human-simulated immune evolutionary algorithm (HSIEA). Considering that the premature convergence problem is due to the lack of diversity in the population, the HSIEA employs the clonal selection principle of artificial immune system theory to preserve the diversity of solutions for the search process. Mathematical descriptions and procedures of the HSIEA are given, and four new evolutionary operators are formulated which are clone, variation, recombination, and selection. Two benchmark optimization functions are investigated to demonstrate the effectiveness of the proposed HSIEA.

An evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles with tactical and kinematic constraints

This paper presents a novel evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles (UAVs) with tactical and kinematic constraints. A genetic algorithm (GA) is modified and extended for path planning. Two GAs are seeded at the initial and final positions with a common objective to minimise their distance apart under given UAV constraints. This is accomplished by the synchronous optimisation of subsequent control vectors. The proposed evolutionary computation approach is called synchronous genetic algorithm (SGA). The sequence of control vectors generated by the SGA constitutes to a near-optimal path plan. The resulting path plan exhibits no discontinuity when transitioning from curve to straight trajectories. Experiments and results show that the paths generated by the SGA are within 2% of the optimal solution. Such a path planner when implemented on a hardware accelerator, such as field programmable gate array chips, can be used in the UAV as on-board replanner, as well as in ground station systems for assisting in high precision planning and modelling of mission scenarios.

A combined evolutionary search and multilevel optimisation approach to graph-partitioning

The graph-partitioning problem is to divide a graph into several pieces so that the number of vertices in each piece is the same within some defined tolerance and the number of cut edges is minimised. Important applications of the problem arise, for example, in parallel processing where data sets need to be distributed across the memory of a parallel machine. Very effective heuristic algorithms have been developed for this problem which run in real-time, but it is not known how good the partitions are since the problem is, in general, NP-complete. This paper reports an evolutionary search algorithm for finding benchmark partitions. A distinctive feature is the use of a multilevel heuristic algorithm to provide an effective crossover. The technique is tested on several example graphs and it is demonstrated that our method can achieve extremely high quality partitions significantly better than those found by the state-of-the-art graph-partitioning packages.

Software Release Planning: An Evolutionary and Iterative Approach

Abstract To achieve higher flexibility and to better satisfy actual customer requirements, there is an increasing tendency to develop and deliver software in an incremental fashion. In adopting this process, requirements are delivered in releases and so a decision has to be made on which requirements should be delivered in which release. Three main considerations that need to be taken account of are the technical precedences inherent in the requirements, the typically conflicting priorities as determined by the representative stakeholders, as well as the balance between required and available effort. The technical precedence constraints relate to situations where one requirement cannot be implemented until another is completed or where one requirement is implemented in the same increment as another one. Stakeholder preferences may be based on the perceived value or urgency of delivered requirements to the different stakeholders involved. The technical priorities and individual stakeholder priorities may be in conflict and difficult to reconcile. This paper provides (i) a method for optimally allocating requirements to increments; (ii) a means of assessing and optimizing the degree to which the ordering conflicts with stakeholder priorities within technical precedence constraints; (iii) a means of balancing required and available resources for all increments; and (iv) an overall method called EVOLVE aimed at the continuous planning of incremental software development. The optimization method used is iterative and essentially based on a genetic algorithm. A set of the most promising candidate solutions is generated to support the final decision. The paper evaluates the proposed approach using a sample project.

An integrated approach to Physcomitrella patens transcriptomics reveals importantclues of the evolutionary development of plant organs and sexual reproduction

Land plant evolution required the generation of a new body plan that could resist the harsher and fluctuating environmental conditions found outside of aquatic environments. Unraveling the genetic basis of plant developmental innovations is not only revealing in terms of an evolutionary point of view, but it is also important for understanding the emergence of agronomically important traits. Comparative genetic studies between basal and modern land plants, both at the genome and trancriptome levels, can help in the generation of hypotheses related to the genetic basis of plant evolutionary development.(...)

Resting metabolic rates of two orbweb spiders: A first approach to evolutionary success of ecribellate spiders

Spiders are considered conservative with regard to their resting metabolic rate, presenting the same allometric relation with body mass as the majority of land-arthropods. Nevertheless, web-building is thought to have a great impact on the energetic metabolism, and any modification that affects this complex behavior is expected to have an impact over the daily energetic budget. We analyzed the possibility of the presence of the cribellum having an effect on the allometric relation between resting metabolic rate and body mass for an ecribellate species (Zosis geniculata) and a cribellate one (Metazygia rogenhoferi), and employed a model selection approach to test if these species had the same allometric relationship as other land-arthropods. Our results show that M. rogenhoferi has a higher resting metabolic rate, while Z. geniculata fitted the allometric prediction for land arthropods. This indicates that the absence of the cribellum is associated with a higher resting metabolic rate, thus explaining the higher promptness to activity found for the ecribellate species. If our result proves to be a general rule among spiders, the radiation of Araneoidea could be connected to a more energy-consuming life style. Thus, we briefly outline an alternative model of diversification of Araneoidea that accounts for this possibility. (C) 2011 Elsevier Ltd. All rights reserved.