Engineering Commons^™

Fitness Biasing For The Box Pushing Task, Gary Parker, Jim O'Connor

Computer Science Faculty Publications

Anytime Learning with Fitness Biasing has been shown in previous works to be an effective tool for evolving hexapod gaits. In this paper, we present the use of Anytime Learning with Fitness Biasing to evolve the controller for a robot learning the box pushing task. The robot that was built for this task, was measured to create an accurate model. The model was used in simulation to test the effectiveness of Anytime Learning with Fitness Biasing for the box pushing task. This work is the first step in new research where an automated system to test the viability of Fitness …

Analytical Comparison Of Multimicrophone Probes In Measuring Acoustic Intensity, Curtis P. Wiederhold

Theses and Dissertations

In the late 1970s, a method was developed to estimate acoustic intensity in one dimension by taking the cross-spectral density of two closely-spaced microphone signals. Since then, multimicrophone probes have been developed to measure three-dimensional intensity as well as energy density. Their usefulness has led to the design of various types of multimicrophone probes, the most common being the four-microphone orthogonal, the four-microphone regular tetrahedron, and the six-microphone designs. These designs generally either consist of microphones suspended in space near each other or mounted on the surface of a sphere. This work analytically compares the relative merits of each probe …

Scalable Co-Evolution Of Soft Robot Properties And Gaits, Davis K. Knox

Honors Theses

The field of soft robotics is very promising; applications in-clude urban search and rescue and covert surveillance, but these projects are not yet realized, partly because of the difficulties in soft robot shape and locomotion design. Be-cause of this, traditional design methods do not prove to be effective. This project attempts to come up with solu-tions to this soft robot design problem; utilizing a genetic algorithm, a computer simulation of Darwin’s “Survival of the Fittest,” this project attempts to make soft bodies move. This genetic algorithm evaluates each solution in simulation, and assigns each one a fitness based on distance …

Experimental And Numerical Development Of Anchor-Jointed Precast Structural Wall System And Optimum Design Of Prestressed Slabs, Mohamed El Semelawy

Electronic Thesis and Dissertation Repository

Precast concrete shear walls are used to resist lateral loads in low- to medium-rise buildings. An innovative joining technique is proposed to accelerate construction process and to reduce concrete damage possibility and capital loss during an earthquake event. Panels are jointed using steel anchor bolts, therefore, the system is designated "anchor-jointed precast structural wall system". In this system, anchors are utilized as a structural fuse. Damaged anchors are easily replaced after an earthquake, thus minimizing repair costs and serviceability disruptions. In the first part of this thesis, conceptual development of the system is carried out. A research program of combined …

Analytical And Numerical Comparisons Of Biogeography-Based Optimization And Genetic Algorithms., Daniel J. Simon, Rick Rarick, Mehmet Ergezer, Dawei Du

Electrical and Computer Engineering Faculty Publications

We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by …

A Hybrid Lehmer Code Genetic Algorithm And Its Application On Traveling Salesman Problems, Jun Zhang

Engineering Management & Systems Engineering Theses & Dissertations

Traveling Salesman Problems (TSP) is a widely studied combinatorial optimization problem. The goal of the TSP is to find a tour which begins in a specific city, visits each of the remaining cities once and returns to the initial cities such that the objective functions are optimized, typically involving minimizing functions like total distance traveled, total time used or total cost.

Genetic algorithms were first proposed by John Holland (1975). It uses an iterative procedure to find the optimal solutions to optimization problems.

This research proposed a hybrid Lehmer code Genetic Algorithm. To compensate for the weaknesses of traditional genetic …

Canal Structure Automation Rules Using An Accuracy-Based Learning Classifier System, A Genetic Algorithm, And A Hydraulic Simulation Model. I: Design, J. E. Hernandez, G. P. Merkley

Jairo E. Hernández

Using state-of-the-art computational techniques, a genetic algorithm (GA) and an accuracy-based learning classifier system (XCS) were shown to produce optimal operational solutions for gate structures in irrigation canals. An XCS successfully developed a set of operational rules for canal gates through the exploration and exploitation of rules using a GA, with the support of an unsteady-state hydraulic simulation model. A computer program which implemented the XCS was used to develop operational rules to operate all canal gate structures simultaneously, while maintaining water depth near target values during variable-demand periods, and with a hydraulically stabilized system when demands no longer changed. …

Canal Structure Automation Rules Using An Accuracy-Based Learning Classifier System, A Genetic Algorithm, And A Hydraulic Simulation Model. Ii: Results, J. E. Hernández, G. P. Merkley

Jairo E. Hernández

An accuracy-based learning classifier system (XCS), as described in a companion paper (Part I: Design), was developed and evaluated to produce operational rules for canal gate structures. The XCS was applied together with a genetic algorithm and an unsteady hydraulic simulation model, which was used to predict responses to gate operation rules. In the tested cases, from 100 to 2,000 XCS simulations, each involving thousands of hydraulic simulations, were required to produce satisfactory rules. However, the overall fitness of the set of rules increased monotonically as XCS simulations progressed. Initial fitness started at an arbitrary value, and rules increased in …

Change Detection Without Difference Image Computation Based On Multiobjective Cost Function Optimization, Turgay Çeli̇k, Zeki̇ Yetgi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we propose a novel method for unsupervised change detection in multi-temporal satellite images by using multiobjective cost function optimization via genetic algorithm (GA). The spatial image grid of the input multi-temporal satellite images is divided into two distinct regions, representing ``changed'' and ``unchanged'' regions between input images, via the intermediate change detection mask produced by the GA. The dissimilarity of pixels of ``changed'' regions and similarity of pixels of ``unchanged'' regions between input multi-temporal images are measured using image quality metrics which consider correlation, spectral distortion, radiometric distortion, and contrast distortion. The contextual information of each pixel …

Using Learning Automata For Multi-Objective Generation Dispatch Considering Cost, Voltage Stability And Power Losses, Ari̇f Karakaş, Celal Kocatepe, Fangxing Li

Turkish Journal of Electrical Engineering and Computer Sciences

The economical and secure operation of power systems has significant importance. Due to technical limitations, the best economical operation point is not always the desired operating point for system stability or power losses. In this study, first, the most economical operating point is obtained by solving the non-linear, network-constrained economic dispatch problem using a genetic algorithm. Then, the system voltage stability is analyzed to compare the different possible operating points using V-Q sensitivity analysis. The power losses, obtained for various operating points, are considered the third objective function. Finally, these 3 aspects of cost, voltage stability, and power losses are …