Analysis of Linkage-Friendly Genetic Algorithms

Evolutionary algorithms (EAs) are stochastic population-based algorithms inspired by the natural processes of selection, mutation, and recombination. EAs are often employed as optimum seeking techniques. A formal framework for EAs is proposed, in which evolutionary operators are viewed as mappings from parameter spaces to spaces of random functions. Formal definitions within this framework capture the distinguishing characteristics of the classes of recombination, mutation, and selection operators. EAs which use strictly invariant selection operators and order invariant representation schemes comprise the class of linkage-friendly genetic algorithms (lfGAs). Fast messy genetic algorithms (fmGAs) are lfGAs which use binary tournament selection (BTS) with thresholding, periodic filtering of a fixed number of randomly selected genes from each individual, and generalized single-point crossover. Probabilistic variants of thresholding and filtering are proposed. EAs using the probabilistic operators are generalized fmGAs (gfmGAs). A dynamical systems model of lfGAs is developed which permits prediction of expected effectiveness. BTS with probabilistic thresholding is modeled at various levels of abstraction as a Markov chain. Transitions at the most detailed level involve decisions between classes of individuals. The probability of correct decision making is related to appropriate maximal order statistics, the distributions of which are obtained. Existing filtering models are extended to include probabilistic individual lengths. Sensitivity of lfGA effectiveness to exogenous parameters limits practical applications. The lfGA parameter selection problem is formally posed as a constrained optimization problem in which the cost functional is related to expected effectiveness. Kuhn-Tucker conditions for the optimality of gfmGA parameters are derived

Cyber Space Odyssey: A Competitive, Team-Oriented Serious Game in Computer Networking

Cyber Space Odyssey (CSO) is a novel serious game supporting computer networking education by engaging students in a race to successfully perform various cybersecurity tasks in order to collect clues and solve a puzzle in virtual near-Earth 3D space. Each team interacts with the game server through a dedicated client presenting a multimodal interface, using a game controller for navigation and various desktop computer networking tools of the trade for cybersecurity tasks on the game\u27s physical network. Specifically, teams connect to wireless access points, use packet monitors to intercept network traffic, decrypt and reverse engineer that traffic, craft well-formed and meaningful responses, and transmit those responses. Successful completion of these physical network actions to solve a sequence of increasingly complex problems is necessary to progress through the virtual, story-driven adventure. Use of the networking tools reinforces networking theory and offers hands-on practical training requisite for today\u27s cyberoperators. This paper presents the learning outcomes targeted by a classroom intervention based on CSO, the design and implementation of the game, a pedagogical overview of the overall intervention, and four years of quantitative and qualitative data assessing its effectiveness

Final Report of the AFIT Quality Initiative External Discovery Committee

This report summarizes the findings of the Air Force Institute of Technology’s (AFIT’s) Quality Initiative - External Discovery Team. The overarching purpose of the Quality Initiative is to create a detailed, executable investment strategy for modernizing AFIT’s instructional capabilities across five thrust areas. These activities were completed over the course of one year, beginning in June of 2016 and concluding in June of 2017. The data gathered were evaluated and several recommendations for further review were decided upon by the External Discovery Team. The following report briefly covers those recommendations and provides sources from which the recommendations were gleaned. These recommendations are meant to serve as a baseline for ways in which AFIT could begin to program resources to help improve teaching and instruction across the institution as a whole. The data presented here are meant to serve as a compliment to the Internal Discovery Team’s report that focuses on data and feedback gathered from institutions internal to AFIT

Axonal Control of the Adult Neural Stem Cell Niche

SUMMARYThe ventricular-subventricular zone (V-SVZ) is an extensive germinal niche containing neural stem cells (NSC) in the walls of the lateral ventricles of the adult brain. How the adult brain’s neural activity influences the behavior of adult NSCs remains largely unknown. We show that serotonergic (5HT) axons originating from a small group of neurons in the raphe form an extensive plexus on most of the ventricular walls. Electron microscopy revealed intimate contacts between 5HT axons and NSCs (B1) or ependymal cells (E1) and these cells were labeled by a transsynaptic viral tracer injected into the raphe. B1 cells express the 5HT receptors 2C and 5A. Electrophysiology showed that activation of these receptors in B1 cells induced small inward currents. Intraventricular infusion of 5HT2C agonist or antagonist increased or decreased V-SVZ proliferation, respectively. These results indicate that supraependymal 5HT axons directly interact with NSCs to regulate neurogenesis via 5HT2C

Abstract

University requirements for the material covered in introductory computer science courses have evolved over the years, and those courses must therefore evolve as well. In this paper, we discuss the 7-year evolution of such a course at the U.S. Air Force Academy. In 1995, the main thrust of the course was to develop students ’ programming skills to support later programming activities, even for those students not majoring in computer science. Although some general survey topics were covered, programming skill development was the main goal of the course. Since that time, the course has evolved significantly into a course that covers general computer science and Information Technology (IT) topics in greater depth and breadth, with a continuing but greatly reduced programming component. During that 7-year period, we changed programming languages for the course, significantly changed the way in which we evaluated programming ability, incorporated graphics into the course, conducted an extensive rework of the course content, and made numerous smaller changes as well. In this paper, we discuss the technical and political issues associated with the evolution of the course. Although this work is presented in the context of our course, such evolution is clearly applicable to other introductory courses as well

A Random Function Based Framework for Evolutionary Algorithms

Evolutionary algorithms (EAs) are stochastic, population-based algorithms inspired by the natural processes of recombination, mutation, and selection. EAs are often employed as optimum seeking techniques. A formal framework for EAs is proposed, in which evolutionary operators are viewed as mappings from parameter spaces to spaces of random functions. Formal de nitions within this framework capture the distinguishing characteristics of the classes of recombination, mutation, and selection operators. A speci c EA, the generalized fast messy genetic algorithm, is de ned within the proposed framework.

Proceedings of the 2005 IEEE Congress on Evolutionary Computation. Multi-Agent Cooperation Using the Ant Algorithm with Variable Pheromone Placement

The Ant Algorithm was created by examining real life ant colonies and developing an algorithm to use the concept of “stigmergy ” to approach multi-agent problems with distributed control. As agents work on tasks, more agents attempt difficult tasks. Task deadlock occurs when agents attempt impossible tasks indefinitely. Previous research avoids task deadlock through adaptive attenuation factors. This research investigates increasing algorithm effectiveness through variable pheromone placement. Results of computational experiments are presented demonstrating the increased effectiveness of the new algorithm. Key Words: multi-agent system, cooperation, ant algorithm, variable pheromone placemen