Parallel Infeasibility Analysis

Oral presentation abstract

Elitist Schema Overlays: A Multi-Parent Genetic Operator

Genetic Algorithms are programs inspired by natural evolution used to solve difficult problems in Mathematics and Computer Science. The theoretical foundations of Genetic Algorithms, the schema theorem and the building-block hypothesis, state that the success of Genetic Algorithms stems from the propagation of fit genetic subsequences. Multi-parent operators were shown to increase the performance of Genetic Algorithms by increasing the disruptivity of genetic operations. Disruptive genetic operators help prevent suboptimal genetic sequences from propagating into future generations, which leads to an improved fitness for the population over time. In this paper we explore the use of a novel multi-parent genetic operator, the elitist schema overlay, which propagates the matching segments in the genetic sequences of the elite subpopulation to bias the global search towards the best known solutions. We investigate the parameters that drive the behavior of elitist schema overlays to determine the most successful model, and we compare this to successful multi-parent and traditional genetic operators from the literature

Contact Angle Measurement

The Contact angle, where a liquid/vapor interface meets a solid surface[wiki], has been widely used to measure the wettability of a surface in physics and chemistry. Scientists place a drop on a surface of interest, take an image of the drop in profile, and measure the angle the drop makes with the surface. We have developed a Contact Angle Measurement plugin for the ImageJ image analysis framework, which provides researchers a easier way to access experiment data. The major goal of our algorithm is to automatically detect drops and surfaces via image analysis, so that we can calculate the contact angle. Firstly, we filter the image by detecting edges and randomly sample a collection of three points on edges to get a collection of circles (note that three points define a circle)which could potentially fit the drop; then we apply various mathematical analyses to adjust the radius and position of the circle to gain a better fit. After the circle detection, we apply linear regression analysis to determine where the surface is. This approach turns out to be very reliable when the input drop region chosen by users is fairly small

Monte Carlo Simulations of Electron Scattering Experiments

This project aims to look at the impact made by certain approximations in electron scattering experiments—specifically whether accounting for these approximation errors is necessary. When using a moveable gun mount, the interaction volume can be determined using a line and cylinder approximation. Data is presented comparing this approximation to the actual volume computed using a Monte Carlo method. A uniform gas distribution is compared to a cosine-squared distribution gas distribution. Additionally, an energy spectrum of a uniform beam is compared to a Gaussian beam for various polarization angles, and a comparison is made between representing the beam as a cone versus a cylinder

Algorithm Visualization

Algorithm visualization is the visual representation of an algorithmic procedure or data structure. It has long been thought by computer science teachers that visualizing algorithms and data structures may lead to better knowledge acquisition in computer science education. However, many studies have been conducted regarding the effectiveness of algorithm visualization, and the results have been mixed. There appear, however, to be traits and features common among studies that have significant positive results. In general, studies that employed active learning, where the learner is mentally engaged with the visualization, often attain significant results. Additionally, studies that pair algorithm visualization with textual or verbal components, a practice known as dual-coding, often have significant results as well. We seek to collect and synthesize current research by looking at surveys and meta-studies of the field and extracting characteristics of algorithm visualization systems that have significant pisitive results. Our goal is to come up with a holistic view of algorithm visualization, including effective features and technologies for implementing visualizations that aid in learning algorithms

An Investigation of Algorithm Visualization

Algorithm visualization, a subfield of computer science research, is the visual representation of an algorithmic procedure or data structure. It often employs multimedia such as videos and animations. It has long been thought by computer science educators that visualizing algorithms and data structures may lead to better knowledge acquisition in computer science education. Several studies have tried to measure the effectiveness of algorithm visualization, and the results have been mixed. However, there appear to be features common among effective algorithm visualizations. Our goals for this project were twofold. First, we sought to synthesize current research by collecting features common among effective algorithm visualizations. Second, we sought to create an algorithm visualization for Professor Mark Liffiton’s MARCO algorithm, employing several of the effective features and technologies we collected. In this work, we present the effective features as well as the algorithm visualization we created for the MARCO algorithm