Use of the q-Gaussian mutation in evolutionary algorithms

This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.

Most of the patients presenting myocardial infarction would not be eligible for intensive lipid-lowering based on clinical algorithms or plasma C-reactive protein

Objective: The study we assessed how often patients who are manifesting a myocardial infarction (MI) would not be considered candidates for intensive lipid-lowering therapy based on the current guidelines. Methods: In 355 consecutive patients manifesting ST elevation MI (STEMI), admission plasma C-reactive protein (CRP) was measured and Framingham risk score (FRS), PROCAM risk score, Reynolds risk score, ASSIGN risk score, QRISK, and SCORE algorithms were applied. Cardiac computed tomography and carotid ultrasound were performed to assess the coronary artery calcium score (CAC), carotid intima-media thickness (cIMT) and the presence of carotid plaques. Results: Less than 50% of STEMI patients would be identified as having high risk before the event by any of these algorithms. With the exception of FRS (9%), all other algorithms would assign low risk to about half of the enrolled patients. Plasma CRP was <1.0 mg/L in 70% and >2 mg/L in 14% of the patients. The average cIMT was 0.8 +/- 0.2 mm and only in 24% of patients was >= 1.0 mm. Carotid plaques were found in 74% of patients. CAC > 100 was found in 66% of patients. Adding CAC >100 plus the presence of carotid plaque, a high-risk condition would be identified in 100% of the patients using any of the above mentioned algorithms. Conclusion: More than half of patients manifesting STEMI would not be considered as candidates for intensive preventive therapy by the current clinical algorithms. The addition of anatomical parameters such as CAC and the presence of carotid plaques can substantially reduce the CVD risk underestimation. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer`s Disease

Here, we examine morphological changes in cortical thickness of patients with Alzheimer`s disease (AD) using image analysis algorithms for brain structure segmentation and study automatic classification of AD patients using cortical and volumetric data. Cortical thickness of AD patients (n = 14) was measured using MRI cortical surface-based analysis and compared with healthy subjects (n = 20). Data was analyzed using an automated algorithm for tissue segmentation and classification. A Support Vector Machine (SVM) was applied over the volumetric measurements of subcortical and cortical structures to separate AD patients from controls. The group analysis showed cortical thickness reduction in the superior temporal lobe, parahippocampal gyrus, and enthorhinal cortex in both hemispheres. We also found cortical thinning in the isthmus of cingulate gyrus and middle temporal gyrus at the right hemisphere, as well as a reduction of the cortical mantle in areas previously shown to be associated with AD. We also confirmed that automatic classification algorithms (SVM) could be helpful to distinguish AD patients from healthy controls. Moreover, the same areas implicated in the pathogenesis of AD were the main parameters driving the classification algorithm. While the patient sample used in this study was relatively small, we expect that using a database of regional volumes derived from MRI scans of a large number of subjects will increase the SVM power of AD patient identification.

Agreement for Detecting Glaucoma Progression with the GDx Guided Progression Analysis, Automated Perimetry, and Optic Disc Photography

Purpose: To evaluate the ability of the GDx Variable Corneal Compensation (VCC) Guided Progression Analysis (GPA) software for detecting glaucomatous progression. Design: Observational cohort study. Participants: The study included 453 eyes from 252 individuals followed for an average of 46 +/- 14 months as part of the Diagnostic Innovations in Glaucoma Study. At baseline, 29% of the eyes were classified as glaucomatous, 67% of the eyes were classified as suspects, and 5% of the eyes were classified as healthy. Methods: Images were obtained annually with the GDx VCC and analyzed for progression using the Fast Mode of the GDx GPA software. Progression using conventional methods was determined by the GPA software for standard automated achromatic perimetry (SAP) and by masked assessment of optic disc stereophotographs by expert graders. Main Outcome Measures: Sensitivity, specificity, and likelihood ratios (LRs) for detection of glaucoma progression using the GDx GPA were calculated with SAP and optic disc stereophotographs used as reference standards. Agreement among the different methods was reported using the AC(1) coefficient. Results: Thirty-four of the 431 glaucoma and glaucoma suspect eyes (8%) showed progression by SAP or optic disc stereophotographs. The GDx GPA detected 17 of these eyes for a sensitivity of 50%. Fourteen eyes showed progression only by the GDx GPA with a specificity of 96%. Positive and negative LRs were 12.5 and 0.5, respectively. None of the healthy eyes showed progression by the GDx GPA, with a specificity of 100% in this group. Inter-method agreement (AC1 coefficient and 95% confidence intervals) for non-progressing and progressing eyes was 0.96 (0.94-0.97) and 0.44 (0.28-0.61), respectively. Conclusions: The GDx GPA detected glaucoma progression in a significant number of cases showing progression by conventional methods, with high specificity and high positive LRs. Estimates of the accuracy for detecting progression suggest that the GDx GPA could be used to complement clinical evaluation in the detection of longitudinal change in glaucoma. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. Ophthalmology 2010; 117: 462-470 (C) 2010 by the American Academy of Ophthalmology.

Relationship between Optical Coherence Tomography, Pattern Electroretinogram and Automated Perimetry in Eyes with Temporal Hemianopia from Chiasmal Compression

PURPOSE. To evaluate the relationship between pattern electroretinogram (PERG) amplitude, macular and retinal nerve fiber layer (RNFL) thickness by optical coherence tomography (OCT), and visual field (VF) loss on standard automated perimetry (SAP) in eyes with temporal hemianopia from chiasmal compression. METHODS. Forty-one eyes from 41 patients with permanent temporal VF defects from chiasmal compression and 41 healthy subjects underwent transient full-field and hemifield (temporal or nasal) stimulation PERG, SAP and time domain-OCT macular and RNFL thickness measurements. Comparisons were made using Student`s t-test. Deviation from normal VF sensitivity for the central 18 of VF was expressed in 1/Lambert units. Correlations between measurements were verified by linear regression analysis. RESULTS. PERG and OCT measurements were significantly lower in eyes with temporal hemianopia than in normal eyes. A significant correlation was found between VF sensitivity loss and fullfield or nasal, but not temporal, hemifield PERG amplitude. Likewise a significant correlation was found between VF sensitivity loss and most OCT parameters. No significant correlation was observed between OCT and PERG parameters, except for nasal hemifield amplitude. A significant correlation was observed between several macular and RNFL thickness parameters. CONCLUSIONS. In patients with chiasmal compression, PERG amplitude and OCT thickness measurements were significant related to VF loss, but not to each other. OCT and PERG quantify neuronal loss differently, but both technologies are useful in understanding structure-function relationship in patients with chiasmal compression. (ClinicalTrials.gov number, NCT00553761.) (Invest Ophthalmol Vis Sci. 2009; 50: 3535-3541) DOI:10.1167/iovs.08-3093

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

Formal validation of automated policy refinement in the management of network security systems

Policy hierarchies and automated policy refinement are powerful approaches to simplify administration of security services in complex network environments. A crucial issue for the practical use of these approaches is to ensure the validity of the policy hierarchy, i.e. since the policy sets for the lower levels are automatically derived from the abstract policies (defined by the modeller), we must be sure that the derived policies uphold the high-level ones. This paper builds upon previous work on Model-based Management, particularly on the Diagram of Abstract Subsystems approach, and goes further to propose a formal validation approach for the policy hierarchies yielded by the automated policy refinement process. We establish general validation conditions for a multi-layered policy model, i.e. necessary and sufficient conditions that a policy hierarchy must satisfy so that the lower-level policy sets are valid refinements of the higher-level policies according to the criteria of consistency and completeness. Relying upon the validation conditions and upon axioms about the model representativeness, two theorems are proved to ensure compliance between the resulting system behaviour and the abstract policies that are modelled.

Optimal subdivision of land in agrarian reform projects: an analysis using genetic algorithms

J.A. Ferreira Neto, E.C. Santos Junior, U. Fra Paleo, D. Miranda Barros, and M.C.O. Moreira. 2011. Optimal subdivision of land in agrarian reform projects: an analysis using genetic algorithms. Cien. Inv. Agr. 38(2): 169-178. The objective of this manuscript is to develop a new procedure to achieve optimal land subdivision using genetic algorithms (GA). The genetic algorithm was tested in the rural settlement of Veredas, located in Minas Gerais, Brazil. This implementation was based on the land aptitude and its productivity index. The sequence of tests in the study was carried out in two areas with eight different agricultural aptitude classes, including one area of 391.88 ha subdivided into 12 lots and another of 404.1763 ha subdivided into 14 lots. The effectiveness of the method was measured using the shunting line standard value of a parceled area lot`s productivity index. To evaluate each parameter, a sequence of 15 calculations was performed to record the best individual fitness average (MMI) found for each parameter variation. The best parameter combination found in testing and used to generate the new parceling with the GA was the following: 320 as the generation number, a population of 40 individuals, 0.8 mutation tax, and a 0.3 renewal tax. The solution generated rather homogeneous lots in terms of productive capacity.

Extending the use of canonical and microcanonical Monte Carlo algorithms to spin models

We describe the canonical and microcanonical Monte Carlo algorithms for different systems that can be described by spin models. Sites of the lattice, chosen at random, interchange their spin values, provided they are different. The canonical ensemble is generated by performing exchanges according to the Metropolis prescription whereas in the microcanonical ensemble, exchanges are performed as long as the total energy remains constant. A systematic finite size analysis of intensive quantities and a comparison with results obtained from distinct ensembles are performed and the quality of results reveal that the present approach may be an useful tool for the study of phase transitions, specially first-order transitions. (C) 2009 Elsevier B.V. All rights reserved.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

PARALLEL ALGORITHMS FOR MAXIMAL CLIQUES IN CIRCLE GRAPHS AND UNRESTRICTED DEPTH SEARCH

We present parallel algorithms on the BSP/CGM model, with p processors, to count and generate all the maximal cliques of a circle graph with n vertices and m edges. To count the number of all the maximal cliques, without actually generating them, our algorithm requires O(log p) communication rounds with O(nm/p) local computation time. We also present an algorithm to generate the first maximal clique in O(log p) communication rounds with O(nm/p) local computation, and to generate each one of the subsequent maximal cliques this algorithm requires O(log p) communication rounds with O(m/p) local computation. The maximal cliques generation algorithm is based on generating all maximal paths in a directed acyclic graph, and we present an algorithm for this problem that uses O(log p) communication rounds with O(m/p) local computation for each maximal path. We also show that the presented algorithms can be extended to the CREW PRAM model.

Approximation algorithms and hardness results for the clique packing problem

For a fixed family F of graphs, an F-packing in a graph G is a set of pairwise vertex-disjoint subgraphs of G, each isomorphic to an element of F. Finding an F-packing that maximizes the number of covered edges is a natural generalization of the maximum matching problem, which is just F = {K(2)}. In this paper we provide new approximation algorithms and hardness results for the K(r)-packing problem where K(r) = {K(2), K(3,) . . . , K(r)}. We show that already for r = 3 the K(r)-packing problem is APX-complete, and, in fact, we show that it remains so even for graphs with maximum degree 4. On the positive side, we give an approximation algorithm with approximation ratio at most 2 for every fixed r. For r = 3, 4, 5 we obtain better approximations. For r = 3 we obtain a simple 3/2-approximation, achieving a known ratio that follows from a more involved algorithm of Halldorsson. For r = 4, we obtain a (3/2 + epsilon)-approximation, and for r = 5 we obtain a (25/14 + epsilon)-approximation. (C) 2008 Elsevier B.V. All rights reserved.

Algorithms for Maximum Independent Set in Convex Bipartite Graphs

A bipartite graph G = (V, W, E) is convex if there exists an ordering of the vertices of W such that, for each v. V, the neighbors of v are consecutive in W. We describe both a sequential and a BSP/CGM algorithm to find a maximum independent set in a convex bipartite graph. The sequential algorithm improves over the running time of the previously known algorithm and the BSP/CGM algorithm is a parallel version of the sequential one. The complexity of the algorithms does not depend on |W|.

Algorithms for two-dimensional cutting stock and strip packing problems using dynamic programming and column generation

We investigate several two-dimensional guillotine cutting stock problems and their variants in which orthogonal rotations are allowed. We first present two dynamic programming based algorithms for the Rectangular Knapsack (RK) problem and its variants in which the patterns must be staged. The first algorithm solves the recurrence formula proposed by Beasley; the second algorithm - for staged patterns - also uses a recurrence formula. We show that if the items are not so small compared to the dimensions of the bin, then these algorithms require polynomial time. Using these algorithms we solved all instances of the RK problem found at the OR-LIBRARY, including one for which no optimal solution was known. We also consider the Two-dimensional Cutting Stock problem. We present a column generation based algorithm for this problem that uses the first algorithm above mentioned to generate the columns. We propose two strategies to tackle the residual instances. We also investigate a variant of this problem where the bins have different sizes. At last, we study the Two-dimensional Strip Packing problem. We also present a column generation based algorithm for this problem that uses the second algorithm above mentioned where staged patterns are imposed. In this case we solve instances for two-, three- and four-staged patterns. We report on some computational experiments with the various algorithms we propose in this paper. The results indicate that these algorithms seem to be suitable for solving real-world instances. We give a detailed description (a pseudo-code) of all the algorithms presented here, so that the reader may easily implement these algorithms. (c) 2007 Elsevier B.V. All rights reserved.

A logic-based agent that plans for extended reachability goals

Planning to reach a goal is an essential capability for rational agents. In general, a goal specifies a condition to be achieved at the end of the plan execution. In this article, we introduce nondeterministic planning for extended reachability goals (i.e., goals that also specify a condition to be preserved during the plan execution). We show that, when this kind of goal is considered, the temporal logic CTL turns out to be inadequate to formalize plan synthesis and plan validation algorithms. This is mainly due to the fact that the CTL`s semantics cannot discern among the various actions that produce state transitions. To overcome this limitation, we propose a new temporal logic called alpha-CTL. Then, based on this new logic, we implement a planner capable of synthesizing reliable plans for extended reachability goals, as a side effect of model checking.