A fuzzy GGA-based approach to speed up the evolutionary process for diverse group stock portfolio optimization1

AbstractGenomes evolve through a medley of mutation, drift, and selection, all of which act heterogeneously across genes and lineages. The pacemaker models of genomic evolution describe the resulting patterns of evolutionary rate variation: genes that are governed by the same pacemaker exhibit the same pattern of rate heterogeneity across lineages. However, the relative importance of drift and selection in determining the structure of these pacemakers is unknown. Here, we propose a novel phylogenetic approach to explain the formation of pacemakers. We apply this method to a genomic dataset from holometabolous insects, an ancient and diverse group of organisms. We show that when drift is the dominant evolutionary process, each pacemaker tends to govern a large number of fast-evolving genes. In contrast, strong negative selection leads to many distinct pacemakers, each of which governs a few slow-evolving genes. Our results provide new insights into the interplay between drift and selection in driving genomic evolution.

Download Full-text

Diverse Group Stock Portfolio Optimization Based on Investor Sentiment Index

2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI) ◽

10.1109/taai.2017.20 ◽

2017 ◽

Author(s):

Chun-Hao Chen ◽

Tzung-pei Hong ◽

Shih-Chi Chu

Keyword(s):

Portfolio Optimization ◽

Investor Sentiment ◽

Diverse Group ◽

Stock Portfolio

Download Full-text

Evolutionary Convolutional Neural Network Optimization with Cross-Tasks Transfer Strategy

Electronics ◽

10.3390/electronics10151857 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1857

Author(s):

Zhao Wang ◽

Di Lu ◽

Huabing Wang ◽

Tongfei Liu ◽

Peng Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Evolutionary Process ◽

Optimization Method ◽

Great Success ◽

Neural Network Optimization ◽

Real World Applications ◽

Speed Up ◽

Classification Tasks

Convolutional neural networks (CNNs) have shown great success in a variety of real-world applications and the outstanding performance of the state-of-the-art CNNs is primarily driven by the elaborate architecture. Evolutionary convolutional neural network (ECNN) is a promising approach to design the optimal CNN architecture automatically. Nevertheless, most of the existing ECNN methods only focus on improving the performance of the discovered CNN architectures without considering the relevance between different classification tasks. Transfer learning is a human-like learning approach and has been introduced to solve complex problems in the domain of evolutionary algorithms (EAs). In this paper, an effective ECNN optimization method with cross-tasks transfer strategy (CTS) is proposed to facilitate the evolution process. The proposed method is then evaluated on benchmark image classification datasets as a case study. The experimental results show that the proposed method can not only speed up the evolutionary process significantly but also achieve competitive classification accuracy. To be specific, our proposed method can reach the same accuracy at least 40 iterations early and an improvement of accuracy for 0.88% and 3.12% on MNIST-FASHION and CIFAR10 datasets compared with ECNN, respectively.

Download Full-text

Scanning x-ray fluorescence microscopy and principal component analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100133394 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1752-1753

Author(s):

Brian Cross

Keyword(s):

Principal Component Analysis ◽

Fluorescence Microscopy ◽

Spatial Resolution ◽

High Speed ◽

Image Acquisition ◽

Principal Component ◽

Fluorescence Microscope ◽

Acquisition Rate ◽

X Ray ◽

Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

Download Full-text

Direct measurement of electron-diffraction-pattern intensities using an energy loss spectrometer

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100155311 ◽

1989 ◽

Vol 47 ◽

pp. 668-669

Author(s):

A. G. Jackson ◽

M. Rowe

Keyword(s):

Thermal Effects ◽

Dynamic Range ◽

Scattering Amplitude ◽

Dynamical Theory ◽

Site Symmetry ◽

Proof Of Concept ◽

Parallel Acquisition ◽

Kinematic Approximation ◽

Speed Up ◽

Structure Calculations

Diffraction intensities from intermetallic compounds are, in the kinematic approximation, proportional to the scattering amplitude from the element doing the scattering. More detailed calculations have shown that site symmetry and occupation by various atom species also affects the intensity in a diffracted beam. [1] Hence, by measuring the intensities of beams, or their ratios, the occupancy can be estimated. Measurement of the intensity values also allows structure calculations to be made to determine the spatial distribution of the potentials doing the scattering. Thermal effects are also present as a background contribution. Inelastic effects such as loss or absorption/excitation complicate the intensity behavior, and dynamical theory is required to estimate the intensity value.The dynamic range of currents in diffracted beams can be 104or 105:1. Hence, detection of such information requires a means for collecting the intensity over a signal-to-noise range beyond that obtainable with a single film plate, which has a S/N of about 103:1. Although such a collection system is not available currently, a simple system consisting of instrumentation on an existing STEM can be used as a proof of concept which has a S/N of about 255:1, limited by the 8 bit pixel attributes used in the electronics. Use of 24 bit pixel attributes would easily allowthe desired noise range to be attained in the processing instrumentation. The S/N of the scintillator used by the photoelectron sensor is about 106 to 1, well beyond the S/N goal. The trade-off that must be made is the time for acquiring the signal, since the pattern can be obtained in seconds using film plates, compared to 10 to 20 minutes for a pattern to be acquired using the digital scan. Parallel acquisition would, of course, speed up this process immensely.

Download Full-text