Ant Colony Optimization for Requirement selection in Incremental Software development

This proposal shows that ACO systems can be applied to problems of requirements selection in software incremental development, with the idea of obtaining better results of those produced by expert judgment alone. The evaluation of the ACO systems should be done through a compared analysis with greedy and simulated annealing algorithms, performing experiments with some problems instances

Multi-objective ant colony optimization for requirements selection

The selection of a set of requirements between all the requirements previously defined by customers is an important process, repeated at the beginning of each development step when an incremental or agile software development approach is adopted. The set of selected requirements will be developed during the actual iteration. This selection problem can be reformulated as a search problem, allowing its treatment with metaheuristic optimization techniques. This paper studies how to apply Ant Colony Optimization algorithms to select requirements. First, we describe this problem formally extending an earlier version of the problem, and introduce a method based on Ant Colony System to find a variety of efficient solutions. The performance achieved by the Ant Colony System is compared with that of Greedy Randomized Adaptive Search Procedure and Non-dominated Sorting Genetic Algorithm, by means of computational experiments carried out on two instances of the problem constructed from data provided by the experts.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

Granulocyte Colony-stimulating Factor (g-csf) Positive Effects On Muscle Fiber Degeneration And Gait Recovery After Nerve Lesion In Mdx Mice.

G-CSF has been shown to decrease inflammatory processes and to act positively on the process of peripheral nerve regeneration during the course of muscular dystrophy. The aims of this study were to investigate the effects of treatment of G-CSF during sciatic nerve regeneration and histological analysis in the soleus muscle in MDX mice. Six-week-old male MDX mice underwent left sciatic nerve crush and were G-CSF treated at 7 days prior to and 21 days after crush. Ten and twenty-one days after surgery, the mice were euthanized, and the sciatic nerves were processed for immunohistochemistry (anti-p75(NTR) and anti-neurofilament) and transmission electron microscopy. The soleus muscles were dissected out and processed for H&E staining and subsequent morphologic analysis. Motor function analyses were performed at 7 days prior to and 21 days after sciatic crush using the CatWalk system and the sciatic nerve index. Both groups treated with G-CSF showed increased p75(NTR) and neurofilament expression after sciatic crush. G-CSF treatment decreased the number of degenerated and regenerated muscle fibers, thereby increasing the number of normal muscle fibers. The reduction in p75(NTR) and neurofilament indicates a decreased regenerative capacity in MDX mice following a lesion to a peripheral nerve. The reduction in motor function in the crushed group compared with the control groups may reflect the cycles of muscle degeneration/regeneration that occur postnatally. Thus, G-CSF treatment increases motor function in MDX mice. Nevertheless, the decrease in baseline motor function in these mice is not reversed completely by G-CSF.

Intrinsic colony conditions affect the provisioning and oviposition process in the stingless bee Melipona scutellaris

The cell provisioning and oviposition process (POP) is a unique characteristic of stingless bees (Meliponini), in which coordinated interactions between workers and queen regulate the filling of brood cells with larval resources and subsequent egg laying. Environmental conditions seem to regulate reproduction in stingless bees; however, little is known about how the amount of food affects quantitative sequences of the process. We examined intrinsic variables by comparing three colonies in distinct conditions (strong, intermediate and weak state). We predicted that some of these variables are correlated with temporal events of POP in Melipona scutellaris colonies. The results demonstrated that the strong colony had shorter periods of POP.

A mixed colony of Scaptotrigona depilis and Nannotrigona testaceicornis (Hymenoptera, Apidae, Meliponina)

We describe a case of a spontaneously established mixed colony of two species of stingless bees. The host colony of Scaptotrigona depilis, an aggressive bee that forms large colonies, was invaded by workers of Nannotrigona testaceicornis, a smaller bee that forms small colonies. The host colony and the invading species colony were maintained in next boxes about 1.5 m apart. The N. testaceicornis colony had been recently divided. Observations were made daily for 10 min, and every two weeks the colony was opened for observations within the nest. Initially the host colony bees repulsed the invading species, but as their numbers built up, they were no longer able to defend the entrance. An estimated 60-90 N. testaceicornis workers lived integrated into the colony of S. depilis for 58 days. During this period, they reconstructed and maintained the entrance tube, changing it to an entrance typical of N. testaceicornis. They also collected food and building material for the host colony. Nannotrigona testaceicornis tolerated transit of S. depilis through the entrance, but did not allow the host species to remain within the tube, though the attacks never resulted in bee mortality. Aggression was limited to biting the wings; when the bees fell to the ground they immediately separated and flew back. There have been very few reports of spontaneously occurring mixed stingless bee colonies. It is difficult to determine what caused the association that we found; probably workers of N. testaceicornis got lost when we split their colony, and then they invaded the colony of S. depilis.

Pollen foraging in colonies of Melipona bicolor (Apidae, Meliponini): effects of season, colony size and queen number

We evaluated the ratio between the number of pollen foragers and the total number of bees entering colonies of Melipona bicolor, a facultative polygynous species of stingless bees. The variables considered in our analysis were: seasonality, colony size and the number of physogastric queens in each colony. The pollen forager ratios varied significantly between seasons; the ratio was higher in winter than in summer. However, colony size and number of queens per colony had no significant effect. We conclude that seasonal differences in pollen harvest are related to the production of sexuals and to the number of individuals and their body size.

First discovery of a rare polygyne colony in the stingless bee Melipona quadrifasciata (Apidae, Meliponini)

FAPESP

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

GOOAL: A routine based on Genetic Algorithms to track harmonics in electric power systems

Voltage and current waveforms of a distribution or transmission power system are not pure sinusoids. There are distortions in these waveforms that can be represented as a combination of the fundamental frequency, harmonics and high frequency transients. This paper presents a novel approach to identifying harmonics in power system distorted waveforms. The proposed method is based on Genetic Algorithms, which is an optimization technique inspired by genetics and natural evolution. GOOAL, a specially designed intelligent algorithm for optimization problems, was successfully implemented and tested. Two kinds of representations concerning chromosomes are utilized: binary and real. The results show that the proposed method is more precise than the traditional Fourier Transform, especially considering the real representation of the chromosomes.

Hybrid Algorithms for Routing and Assignment Wavelengths in Optical Networks

This paper presents a strategy for the solution of the WDM optical networks planning. Specifically, the problem of Routing and Wavelength Allocation (RWA) in order to minimize the amount of wavelengths used. In this case, the problem is known as the Min-RWA. Two meta-heuristics (Tabu Search and Simulated Annealing) are applied to take solutions of good quality and high performance. The key point is the degradation of the maximum load on the virtual links in favor of minimization of number of wavelengths used; the objective is to find a good compromise between the metrics of virtual topology (load in Gb/s) and of the physical topology (quantity of wavelengths). The simulations suggest good results when compared to some existing in the literature.

Information Filtering and Array Algorithms for Discrete-Time Markovian Jump Linear Systems

This technical note develops information filter and array algorithms for a linear minimum mean square error estimator of discrete-time Markovian jump linear systems. A numerical example for a two-mode Markovian jump linear system, to show the advantage of using array algorithms to filter this class of systems, is provided.

A Taxonomy for Locality Algorithms on Peer-to-Peer Networks

The continuous growth of peer-to-peer networks has made them responsible for a considerable portion of the current Internet traffic. For this reason, improvements in P2P network resources usage are of central importance. One effective approach for addressing this issue is the deployment of locality algorithms, which allow the system to optimize the peers` selection policy for different network situations and, thus, maximize performance. To date, several locality algorithms have been proposed for use in P2P networks. However, they usually adopt heterogeneous criteria for measuring the proximity between peers, which hinders a coherent comparison between the different solutions. In this paper, we develop a thoroughly review of popular locality algorithms, based on three main characteristics: the adopted network architecture, distance metric, and resulting peer selection algorithm. As result of this study, we propose a novel and generic taxonomy for locality algorithms in peer-to-peer networks, aiming to enable a better and more coherent evaluation of any individual locality algorithm.

Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms

In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees. (C) 2009 Elsevier B.V. All rights reserved.