Alexandre César Muniz de Oliveira

Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM

Female breast cancer is the major cause of cancer-related deaths in western countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. In this paper, we present a methodology that uses Morans index and Gearys coefficient measures in breast tissues extracted from mammogram images. These measures are used as input features for a support vector machine classifier with the purpose of distinguishing tissues between normal and abnormal cases as well as classifying them into benign and malignant cancerous cases. The use of both proposed techniques showed to be very promising, since we obtained an accuracy of 96.04% and Az ROC of 0.946 with Gearys coefficient and an accuracy of 99.39% and Az ROC of 1 with Morans index to discriminate tissues in mammograms as normal or abnormal. We also obtained accuracy of 88.31% and Az ROC of 0.804 with Gearys coefficient and accuracy of 87.80% and Az ROC of 0.89 with Morans index to discriminate tissues in mammograms as benign and malignant.

A hybrid column generation approach for the berth allocation problem

Blends of polyphenylene oxide or styrene resin-modified polyphenylene oxide, lactone block copolymers and, optionally, polar resins exhibiting improved impact strength as compared with polyphenylene oxide resin with no additive.

Hybrid Evolutionary Algorithms and Clustering Search

Summary. A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas. The inspiration in nature has been pursued to design flexible, coherent, and efficient computational models. In this chapter, the clustering search (*CS) is proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process aims to gather similar information about the search space into groups, maintaining a representative solution associated to this information. Two applications are examined for combinatorial and continuous optimization problems, presenting how to develop hybrid evolutionary algorithms based on *CS.

Detecting Promising Areas by Evolutionary Clustering Search

A challenge in hybrid evolutionary algorithms is to define efficient strategies to cover all search space, applying local search only in actually promising search areas. This paper proposes a way of detecting promising search areas based on clustering. In this approach, an iterative clustering works simultaneously to an evolutionary algorithm accounting the activity (selections or updatings) in search areas and identifying which of them deserves a special interest. The search strategy becomes more aggressive in such detected areas by applying local search. A first application to unconstrained numerical optimization is developed, showing the competitiveness of the method.

Population training heuristics

This work describes a new way of employing problem-specific heuristics to improve evolutionary algorithms: the Population Training Heuristic (PTH). The PTH employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals can not be improved by such heuristics. Some new theoretical improvements not present in early algorithms are now introduced. An application for pattern sequencing problems is examined with new improved computational results. The method is also compared against other approaches, using benchmark instances taken from the literature.

Semivariogram applied for classification of benign and malignant tissues in mammography

This work analyzes the application of the semivariogram function to the characterization of breast tissue as malignant or benign in mammographic images. The method characterization is based on a process that selects, using stepwise technique, from all computed semivariance which best discriminate between the benign and malignant tissues. Then, a multilayer perceptron neural network is used to evaluate the ability of these features to predict the classification for each tissue sample. To verify this application we also describe tests that were carried out using a set of 117 tissues samples, 67 benign and 50 malignant. The result analysis has given a sensitivity of 92.8%, a specificity of 83.3% and an accuracy above 88.0%, which means encouraging results. The preliminary results of this approach are very promising in characterizing breast tissue.

Opt Population Training for Minimization of Open Stack Problem

This paper describes an application of a Constructive Genetic Algorithm (CGA) to the Minimization Open Stack Problem (MOSP). The MOSP happens in a production system scenario, and consists of determining a sequence of cut patterns that minimizes the maximum number of opened stacks during the cutting process. The CGA has a number of new features compared to a traditional genetic algorithm, as a population of dynamic size composed of schemata and structures that is trained with respect to some problem specific heuristic. The application of CGA to MOSP uses a 2-Opt like heuristic to define the fitness functions and the mutation operator. Computational tests are presented using available instances taken from the literature.

Pattern sequencing problems by clustering search

Modern search methods for optimization consider hybrid search metaheuristics those employing general optimizers working together with a problem-specific local search procedure. The hybridism comes from the balancing of global and local search procedures. A challenge in such algorithms is to discover efficient strategies to cover all the search space, applying local search only in actually promising search areas. This paper proposes the Clustering Search (*CS): a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process aims to gather similar information about the problem at hand into groups, maintaining a representative solution associated to this information. Two applications to combinatorial optimization are examined, showing the flexibility and competitiveness of the method.

Classification of breast tissues using Getis-Ord statistics and support vector machine

Female breast cancer is the major cause of cancer-related deaths in western countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. In this paper, we present a methodology that intends to use Getis Index spatial texture measures in order to distinguish mass and non-mass tissues extracted from mammograms. The computed measures are classified through a One-Class and a Two-Class Support Vector Machine (SVM). The proposed method reaches 99.33% of accuracy using One-Class SVM and 94.21% of accuracy using Two-Class SVM.

Clustering Search and Variable Mesh Algorithms for continuous optimization

A hybrid meta-heuristic is proposed based on Clustering Search.The Variable Mesh Optimization generates initial solutions.Clustering Search algorithm detect promising areas in the solution space.Local search improves a solution called the center of each cluster.The hybrid proposal shows to be beneficial for continuous optimization problems. The hybridization of population-based meta-heuristics and local search strategies is an effective algorithmic proposal for solving complex continuous optimization problems. Such hybridization becomes much more effective when the local search heuristics are applied in the most promising areas of the solution space. This paper presents a hybrid method based on Clustering Search (CS) to solve continuous optimization problems. The CS divides the search space in clusters, which are composed of solutions generated by a population meta-heuristic, called Variable Mesh Optimization. Each cluster is explored further with local search procedures. Computational results considering a benchmark of multimodal continuous functions are presented.