A Framework for Fast Classification Algorithms

Today, due to globalization of the world the size of data set is increasing, it is necessary to discover the knowledge. The discovery of knowledge can be typically in the form of association rules, classification rules, clustering, discovery of frequent episodes and deviation detection. Fast and accurate classifiers for large databases are an important task in data mining. There is growing evidence that integrating classification and association rules mining, classification approaches based on heuristic, greedy search like decision tree induction. Emerging associative classification algorithms have shown good promises on producing accurate classifiers. In this paper we focus on performance of associative classification and present a parallel model for classifier building. For classifier building some parallel-distributed algorithms have been proposed for decision tree induction but so far no such work has been reported for associative classification.

The Production Scheduling in Assembly System with Evolutionary Algorithm

In this paper an evolutionary algorithm is proposed for solving the problem of production scheduling in assembly system. The aim of the paper is to investigate a possibility of the application of evolutionary algorithms in the assembly system of a normally functioning enterprise producing household appliances to make the production graphic schedule.

Building Better Nurse Scheduling Algorithms

The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification.

Building Better Nurse Scheduling Algorithms

The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification.

Building Better Nurse Scheduling Algorithms

The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification.

Phylogenetic inference's algorithms

Phylogenetic inference consist in the search of an evolutionary tree to explain the best way possible genealogical relationships of a set of species. Phylogenetic analysis has a large number of applications in areas such as biology, ecology, paleontology, etc. There are several criterias which has been defined in order to infer phylogenies, among which are the maximum parsimony and maximum likelihood. The first one tries to find the phylogenetic tree that minimizes the number of evolutionary steps needed to describe the evolutionary history among species, while the second tries to find the tree that has the highest probability of produce the observed data according to an evolutionary model. The search of a phylogenetic tree can be formulated as a multi-objective optimization problem, which aims to find trees which satisfy simultaneously (and as much as possible) both criteria of parsimony and likelihood. Due to the fact that these criteria are different there won't be a single optimal solution (a single tree), but a set of compromise solutions. The solutions of this set are called "Pareto Optimal". To find this solutions, evolutionary algorithms are being used with success nowadays.This algorithms are a family of techniques, which aren’t exact, inspired by the process of natural selection. They usually find great quality solutions in order to resolve convoluted optimization problems. The way this algorithms works is based on the handling of a set of trial solutions (trees in the phylogeny case) using operators, some of them exchanges information between solutions, simulating DNA crossing, and others apply aleatory modifications, simulating a mutation. The result of this algorithms is an approximation to the set of the “Pareto Optimal” which can be shown in a graph with in order that the expert in the problem (the biologist when we talk about inference) can choose the solution of the commitment which produces the higher interest. In the case of optimization multi-objective applied to phylogenetic inference, there is open source software tool, called MO-Phylogenetics, which is designed for the purpose of resolving inference problems with classic evolutionary algorithms and last generation algorithms. REFERENCES [1] C.A. Coello Coello, G.B. Lamont, D.A. van Veldhuizen. Evolutionary algorithms for solving multi-objective problems. Spring. Agosto 2007 [2] C. Zambrano-Vega, A.J. Nebro, J.F Aldana-Montes. MO-Phylogenetics: a phylogenetic inference software tool with multi-objective evolutionary metaheuristics. Methods in Ecology and Evolution. En prensa. Febrero 2016.

Features of colorectal cancers with high-level microsatellite instability occurring in familial and sporadic settings

High-level microsatellite instability (AISI-H) is demonstrated in 10 to 15% of sporadic colorectal cancers and in most cancers presenting In the inherited condition hereditary nonpolyposis colorectal cancer (HNPCC). Distinction between these categories of MSI-H cancer is of clinical importance and the aim of this study was to assess clinical, pathological, and molecular features that might he discriminatory. One hundred and twelve MSI-H colorectal cancers from families fulfilling the Bethesda criteria were compared with 57 sporadic MSI-H colorectal cancers. HNPCC cancers presented at a lower age (P < 0.001) with no sporadic MSI-H cancer being diagnosed before the age of 57 years. MSI was less extensive in HNPCC cancers with 72% microsatellite markers showing band shifts compared with 87% in sporadic tumors (P < 0.001). Absent immunostaining for hMSH2 was only found in HNPCC tumors. Methylation of bMLH1 was observed in 87% of sporadic cancers but also in 55% of HNPCC tumors that showed loss of expression of hMLH1 (P = 0.02). HNPCC cancers were more frequently characterized by aberrant beta -catenin immunostaining as evidenced by nuclear positivity (P < 0.001). Aberrant p53 immunostaining was infrequent in both groups. There were no differences with respect to 5q loss of heterozygosity or codon 12 K-ras mutation, which were infrequent in both groups. Sporadic MSI-H cancers were more frequently heterogeneous (P < 0.001), poorly differentiated (P = 0.02), mucinous (P = 0.02), and proximally located (P = 0.04) than RNPCC tumors. In sporadic MSI-H cancers, contiguous adenomas were likely to be serrated whereas traditional adenomas were dominant in HNPCC. Lymphocytic infiltration was more pronounced in HNPCC but the results did not reach statistical significance. Overall, HNPCC cancers were more like common colorectal cancer in terms of morphology and expression of beta -catenin whereas sporadic MSI-H cancers displayed features consistent with a different morphogenesis. No individual feature was discriminatory for all RN-PCC cancers. However, a model based on four features was able to classify 94.5% of tumors as sporadic or HNPCC. The finding of multiple differences between sporadic and familial MSI-H colorectal cancer with respect to both genotype and phenotype is consistent with tumorigenesis through parallel evolutionary pathways and emphasizes the importance of studying the two groups separately.

Direct multisearch for multiobjective optimization

In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible from the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular, when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single objective optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational experience has shown that our methodology has an impressive capability of generating the whole Pareto front, even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a comparison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts.

Algoritmos genéticos: aplicação na síntese de alguns algoritmos de controlo

Mestrado em Engenharia Electrotécnica e de Computadores. Área de Especialização de Automação e Sistemas.

Análise e síntese de música através de sistemas computacionais

Este trabalho, realizado no âmbito da unidade curricular de Tese/Dissertação, procura mostrar de que forma a Computação Evolucionária se pode aplicar no mundo da Música. Este é, de resto, um tema sobejamente aliciante dentro da área da Inteligência Artificial. Começa-se por apresentar o mundo da Música com uma perspetiva cronológica da sua história, dando especial relevo ao estilo musical do Fado de Coimbra. Abordam-se também os conceitos fundamentais da teoria musical. Relativamente à Computação Evolucionária, expõem-se os elementos associados aos Algoritmos Evolucionários e apresentam-se os principais modelos, nomeadamente os Algoritmos Genéticos. Ainda no âmbito da Computação Evolucionária, foi elaborado um pequeno estudo do “estado da arte” da aplicação da Computação Evolucionária na Música. A implementação prática deste trabalho baseia-se numa aplicação – AG Fado – que compõe melodias de Fado de Coimbra, utilizando Algoritmos Genéticos. O trabalho foi dividido em duas partes principais: a primeira parte consiste na recolha de informações e posterior levantamento de dados estatísticos sobre o género musical escolhido, nomeadamente fados em tonalidade maior e fados em tonalidade menor; a segunda parte consiste no desenvolvimento da aplicação, com a conceção do respetivo algoritmo genético para composição de melodias. As melodias obtidas através da aplicação desenvolvida são bastante audíveis e boas melodicamente. No entanto, destaca-se o facto de a avaliação ser efetuada por seres humanos o que implica sensibilidades musicais distintas levando a resultados igualmente distintos.

Algoritmos evolutivos na determinação das máximas injeções nodais em redes de energia elétrica

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica - Ramo de Energia

Metaheuristic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Metaheuristhic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Multi-criteria optimization manipulator trajectory planning

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at destination point. Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development of an algorithm to find a continuous motion that takes the manipulator from a given starting configuration up to a desired end position without colliding with any obstacle in the workspace. The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches proposed in the literature. Section 3 formulates the problem, namely the representation adopted to solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links) when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for the 3R redundant manipulator with five goals and for other complementary experiments are described, respectively. Finally, section 8 draws the main conclusions.

Fractional calculus: application in modelling and control

This contribution introduces the fractional calculus (FC) fundamental mathematical aspects and discuses some of their consequences. Based on the FC concepts, the chapter reviews the main approaches for implementing fractional operators and discusses the adoption of FC in control systems. Finally are presented some applications in the areas of modeling and control, namely fractional PID, heat diffusion systems, electromagnetism, fractional electrical impedances, evolutionary algorithms, robotics, and nonlinear system control.