Considerations for Rapidly Converging Genetic Algorithms Designed for Application to Problems with Expensive Evaluation Functions

A genetic algorithm is a technique designed to search large problem spaces using the Darwinian concepts of evolution. Solution representations are treated as living organisms. The procedure attempts to evolve increasingly superior solutions. As in natural genetics, however, there is no guarantee that the optimum organism will be produced. One of the problems in producing optimal organisms in a genetic algorithm is the difficulty of premature convergence. Premature convergence occurs when the organisms converge in similarity to a pattern which is sub-optimal, but insufficient genetic material is present to continue the search beyond this sub-optimal level, called a local maximum. The prevention of premature convergence of the organisms is crucial to the success of most genetic algorithms. In order to prevent such convergence, numerous operators have been developed and refined. All such operators, however, rely on the property of the underlying problem that the evaluation of individuals is a computationally inexpensive process. In this paper, the design of genetic algorithms which intentionally converge rapidly is addressed. The design considerations are outlined, and the concept is applied to an NP-Complete problem, known as a Crozzle, which does not have an inexpensive evaluation function. This property would normally make the Crozzle unsuitable for processing by a genetic algorithm. It is shown that a rapidly converging genetic algorithm can successfully reduce the effective complexity of the problem

PROGRAM and PROCEEDINGS THE NEBRASKA ACADEMY OF SCIENCES 1880-2017 Including the Nebraska Association of Teachers of Science (NATS) Division Nebraska Junior Academy of Sciences (NJAS) Affiliate and Affiliated Societies

FRIDAY, APRIL 21, 2017 7:30 a.m. REGISTRATION FOR ACADEMY, Lobby of Lecture wing, Olin Hall 8:00 Aeronautics and Space Science, Session A, Olin 249 Aeronautics and Space Science, Session B, Olin 224 Chemistry and Physics, Section A, Chemistry, Olin A Collegiate Academy, Biology, Session A, Olin B Collegiate Academy, Biology, Session B, Olin 112 Collegiate Academy, Chemistry and Physics, Session A, Olin 324 8:30 Biological and Medical Sciences, Session A, Smith Callen Conference Center 9:10 Aeronautics and Space Science, Poster Session, Olin 249 9:40 Applied Science and Technology, Olin 325 10:00 Chemistry and Physics, Physics, Section B, Planetarium 10:30 Aeronautics and Space Science, Poster Session, Olin 249 11:00 MAIBEN MEMORIAL LECTURE, OLIN B – Scholarship and Friend of Science Recipients also announced. 12:00 LUNCH, PATIO ROOM, STORY STUDENT CENTER Aeronautics Group, Sunflower Room 1:00 p.m. Anthropology, Olin 111 Biological and Medical Sciences, Session B, Smith Callen Conference Center Collegiate Academy, Biology, Session A, Olin B Collegiate Academy, Biology, Session B, Olin 112 Collegiate Academy, Chemistry and Physics, Session B, Olin 324 Earth Science, Olin 249 1:05 Applied Science and Technology, Olin 325 1:15 Teaching of Science and Math, Olin 224 Chemistry and Physics, Section A, Chemistry, Olin A 2:45 Environmental Sciences, Olin 249 4:30 BUSINESS MEETING, OLIN B Abstracts of papers 2016-2017 EXECUTIVE COMMITTEE 2016-2017 PROGRAM COMMITTEE 2016-2017 POLICY COMMITTEE FRIENDS OF THE ACADEMY FRIEND OF SCIENCE AWARD WINNERS FRIEND OF SCIENCE AWARD TO KACIE BAUM FRIEND OF SCIENCE AWARD TO TODD YOUNG Author Index 141 p

CLiFF Notes: Research in the Language Information and Computation Laboratory of The University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLIFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science, Psychology, and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. With 48 individual contributors and six projects represented, this is the largest LINC Lab collection to date, and the most diverse

Linguistic Representation of Problem Solving Processes in Unaided Object Assembly

This thesis investigates the linguistic representation of problem solving processes in data recorded during unaided object assembly. It combines traditional approaches of analyzing verbal protocols with the recent approach of Cognitive Discourse Analysis

CLiFF Notes: Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

One concern of the Computer Graphics Research Lab is in simulating human task behavior and understanding why the visualization of the appearance, capabilities and performance of humans is so challenging. Our research has produced a system, called Jack, for the definition, manipulation, animation and human factors analysis of simulated human figures. Jack permits the envisionment of human motion by interactive specification and simultaneous execution of multiple constraints, and is sensitive to such issues as body shape and size, linkage, and plausible motions. Enhanced control is provided by natural behaviors such as looking, reaching, balancing, lifting, stepping, walking, grasping, and so on. Although intended for highly interactive applications, Jack is a foundation for other research. The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object around us, and yet the most structurally complex. Their everyday movements are amazingly fluid, yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language. Present technology lets us approach human appearance and motion through computer graphics modeling and three dimensional animation, but there is considerable distance to go before purely synthesized figures trick our senses. We seek to build computational models of human like figures which manifest animacy and convincing behavior. Towards this end, we: Create an interactive computer graphics human model; Endow it with reasonable biomechanical properties; Provide it with human like behaviors; Use this simulated figure as an agent to effect changes in its world; Describe and guide its tasks through natural language instructions. There are presently no perfect solutions to any of these problems; ultimately, however, we should be able to give our surrogate human directions that, in conjunction with suitable symbolic reasoning processes, make it appear to behave in a natural, appropriate, and intelligent fashion. Compromises will be essential, due to limits in computation, throughput of display hardware, and demands of real-time interaction, but our algorithms aim to balance the physical device constraints with carefully crafted models, general solutions, and thoughtful organization. The Jack software is built on Silicon Graphics Iris 4D workstations because those systems have 3-D graphics features that greatly aid the process of interacting with highly articulated figures such as the human body. Of course, graphics capabilities themselves do not make a usable system. Our research has therefore focused on software to make the manipulation of a simulated human figure easy for a rather specific user population: human factors design engineers or ergonomics analysts involved in visualizing and assessing human motor performance, fit, reach, view, and other physical tasks in a workplace environment. The software also happens to be quite usable by others, including graduate students and animators. The point, however, is that program design has tried to take into account a wide variety of physical problem oriented tasks, rather than just offer a computer graphics and animation tool for the already computer sophisticated or skilled animator. As an alternative to interactive specification, a simulation system allows a convenient temporal and spatial parallel programming language for behaviors. The Graphics Lab is working with the Natural Language Group to explore the possibility of using natural language instructions, such as those found in assembly or maintenance manuals, to drive the behavior of our animated human agents. (See the CLiFF note entry for the AnimNL group for details.) Even though Jack is under continual development, it has nonetheless already proved to be a substantial computational tool in analyzing human abilities in physical workplaces. It is being applied to actual problems involving space vehicle inhabitants, helicopter pilots, maintenance technicians, foot soldiers, and tractor drivers. This broad range of applications is precisely the target we intended to reach. The general capabilities embedded in Jack attempt to mirror certain aspects of human performance, rather than the specific requirements of the corresponding workplace. We view the Jack system as the basis of a virtual animated agent that can carry out tasks and instructions in a simulated 3D environment. While we have not yet fooled anyone into believing that the Jack figure is real , its behaviors are becoming more reasonable and its repertoire of actions more extensive. When interactive control becomes more labor intensive than natural language instructional control, we will have reached a significant milestone toward an intelligent agent

Interpersonal deceit and lie-detection using computer-mediated communication

This thesis examines the use of computer-mediated communication for lie-detection and interpersonal deceit. The literature within the fields of lie-detection and mediated communication are reviewed and it is proposed that there is a lack of knowledge surrounding how people use CMC to deceive one another. Qualitative research was carried out in order to address this shortcoming, exploring the self-reported experiences of chat room users who have been exposed to online deceit. Reports were provided that describe the misrepresentation of age, gender, vocation, affection, and appearance. The importance of stereotypes in driving suspicions is also emphasised within the reports. It is suggested that this key characteristic has more dominance in CMC than it would do face-to-face because of the occlusion of the traditional nonstrategic clues to deceit. Evidence for an alternative set of nonstrategic leakage clues was examined further by conducting a variant of the Guilty-Knowledge test within the context of a CMC based crime. It was found that participants exhibited a response time inhibition effect when presented with 'guilty knowledge' and that this effect was detectable through a standard two-button mouse. The use of such nonstrategic cues to deceit was explored further in a study that examined how CMC might be used to add additional control to a Statement Validity Assessment truth-validation test. It was found that the content analysis technique used by SVA was unable in its present form to correctly distinguish between truthful and fabricated statements of participants interviewed using a CMC chat program. In addition, it was found that the deletion-behaviours of participants fabricating a story within CMC provided no quantitative or qualitative evidence that they were lying

Learning Spaces

Edited by Diana G. Oblinger. Includes a chapter by former College at Brockport Faculty member Joan K. Lippincott: Linking the Information Commons to learning. Space, whether physical or virtual, can have a significant impact on learning. Learning Spaces focuses on how learner expectations influence such spaces, the principles and activities that facilitate learning, and the role of technology from the perspective of those who create learning environments: faculty, learning technologists, librarians, and administrators. Information technology has brought unique capabilities to learning spaces, whether stimulating greater interaction through the use of collaborative tools, videoconferencing with international experts, or opening virtual worlds for exploration. This e-book represents an ongoing exploration as we bring together space, technology, and pedagogy to ensure learner success.https://digitalcommons.brockport.edu/bookshelf/1077/thumbnail.jp

Artificial intelligence and its application in architectural design

No abstract available.No abstract available