Landscape state machines: Tools for evolutionary algorithm performance analyses and landscape/algorithm mapping

Many evolutionary algorithm applications involve either fitness functions with high time complexity or large dimensionality (hence very many fitness evaluations will typically be needed) or both. In such circumstances, there is a dire need to tune various features of the algorithm well so that performance and time savings are optimized. However, these are precisely the circumstances in which prior tuning is very costly in time and resources. There is hence a need for methods which enable fast prior tuning in such cases. We describe a candidate technique for this purpose, in which we model a landscape as a finite state machine, inferred from preliminary sampling runs. In prior algorithm-tuning trials, we can replace the 'real' landscape with the model, enabling extremely fast tuning, saving far more time than was required to infer the model. Preliminary results indicate much promise, though much work needs to be done to establish various aspects of the conditions under which it can be most beneficially used. A main limitation of the method as described here is a restriction to mutation-only algorithms, but there are various ways to address this and other limitations.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Review of "Robot: The Future of Flesh and Machines" by Rodney A. Brooks

Rodney Brooks has been called the “Self Styled Bad Boy of Robotics”. In the 1990s he gained this dubious honour by orchestrating a string of highly evocative robots from his artificial interligence Labs at the Massachusettes Institute of Technology (MIT), Boston, USA.

Speed sensorless vector control of induction machines

A novel rotor velocity estimation scheme applicable to vector controlled induction motors has been described. The proposed method will evaluate rotor velocity, ωr, on-line, does not require any extra transducers or injection of any signals, nor does it employ complicated algorithms such as MRAS or Kalman filters. Furthermore, the new scheme will operate at all velocities including zero with very little error. The procedure employs motor model equations, however all differential and integral terms have been eliminated giving a very fast, low-cost, effective and practical alternative to the current available methods. Simulation results verify the operation of the scheme under ideal and PWM conditions.

Measurement of apolipoprotein B-48 in the Svedberg flotation rate (Sf) > 400, Sf 60 – 400 and Sf 20 – 60 lipoprotein fractions reveals novel findings with respect to the effects of dietary fatty acids on triacylglycerol-rich lipoproteins in postmenopausal women

The present study was designed to examine whether the type of fat ingested in an initial test meal influences the response and density distribution of dietary-derived lipoproteins in the Svedberg flotation rate (Sf)>400, Sf 60 - 400 and Sf 20 - 60 lipoprotein fractions. A single-blind randomized within-subject crossover design was used to study the effects of palm oil, safflower oil, a mixture of fish and safflower oil, and olive oil on postprandial apolipoprotein (apo) B-48, retinyl ester and triacylglycerol responses in each lipoprotein fraction following an initial test meal containing one of the oils and a second standardized test meal. For all dietary oils, late postprandial (300min) concentrations of triacylglycerol and apo B-48 were significantly higher in the Sf 60 - 400 fraction than in the Sf>400 fraction (P<0.02). Significantly greater apo B-48 incremental areas under the curve (IAUCs) were also observed in the Sf 60 - 400 fraction than in the Sf>400 fraction following palm oil, safflower oil and olive oil (P<0.04), with a similar non-significant trend for fish/safflower oil. Olive oil resulted in a significantly greater apo B-48 IAUC in the Sf>400 fraction (P<0.02) than did any of the other dietary oils, as well as a tendency for a higher IAUC in the Sf 60 - 400 fraction compared with the palm, safflower and fish/safflower oils. In conclusion, we have found that the majority of intestinally derived lipoproteins present in the circulation following meals enriched with saturated, polyunsaturated or monounsaturated fatty acids are of the density and size of small chylomicrons and chylomicron remnants. Olive oil resulted in a greater apo B-48 response compared with the other dietary oils following sequential test meals, suggesting the formation of a greater number of small (Sf 60 - 400) and large (Sf>400) apo B-48-containing lipoproteins in response to this dietary oil.

Separation of astaxanthin from cells of Phaffia rhodozyma using colloidal gas aphrons (CGA) in a flotation column

The aim of this study is to investigate the separation of astaxanthin from the cells of Phaffia rhodozyma using colloidal gas aphrons (CGA), which are surfactant stabilized microbubbles, in a flotation column. It was reported in previous studies that optimum recoveries are achieved at conditions that favor electrostatic interactions. Therefore, in this study, CGA generated from the cationic surfactant hexadecyl trimethyl ammonium bromide (CTAB) were applied to suspensions of cells pretreated with NaOH. The different operation modes (batch or continuous) and the effect of volumetric ratio of CGA to feed, initial concentration of feed, operating height, and flow rate of CGA on the separation of astaxanthin were investigated. The volumetric ratio was found to have a significant effect on the separation of astaxanthin for both batch and continuous experiments. Additionally, the effect of homogenization of the cells on the purity of the recovered fractions was investigated, showing that the homogenization resulted in increased purity. Moreover, different concentrations of surfactant were used for the generation of CGA for the recovery of astaxanthin on batch mode; it was found that recoveries up to 98% could be achieved using CGA generated from a CTAB solution 0.8 mM, which is below the CTAB critical micellar concentration (CMC). These results offer important information for the scale-up of the separation of astaxanthin from the cells of P. rhodozyma using CGA.

Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.