Digital Commons Network^™

A Genetic Algorithms Approach To Non-Coding Rna Gene Searches, Jennifer A. Smith

Electrical and Computer Engineering Faculty Publications and Presentations

A genetic algorithm is proposed as an alternative to the traditional linear programming method for scoring covariance models in non-coding RNA (ncRNA) gene searches. The standard method is guaranteed to find the best score, but it is too slow for general use. The observation that most of the search space investigated by the linear programming method does not even remotely resemble any observed sequence in real sequence data can be used to motivate the use of genetic algorithms (GAs) to quickly reject regions of the search space. A search space with many local minima makes gradient decent an unattractive alternative. …

Prescriptive Treatment Optimization Using A Genetic Algorithm: A Tool For Forest Management, John Dewey, Frederick Maier, Walter D. Potter, Donald Nute, H. M. Rauscher, Mark Twery, Peter Knopp, Scott Thomasma

International Congress on Environmental Modelling and Software

This paper describes research on the use of a multiobjective genetic algorithm (GA) to optimize prescriptive treatment plans for forest management. The algorithm is novel, in that (1) the plans generated by the algorithm are highly specific, stating precisely when and where treatments are to be applied; and (2) logical rules and inference engines developed for a decision support system are used to evaluate the fitness of each plan. Fitness is based upon satisfaction of varied and often incompatible goals. The current (generational) GA has been compared in experiments to hill-climbing and simulated annealing algorithms, as well as to a …

New Directions And Challenges In Spatial Dynamic Modelling Of Ecosystem Functions In Heterogeneous Landscapes As Basis For A Better Sustainable Landscape Management, Karl-Otto Wenkel, Ralf Wieland, Wilfried Mirschel

International Congress on Environmental Modelling and Software

The aim of this paper is to discuss scientific challenges and new possibilities for a better modelling of consequences of land use changes in heterogeneous landscapes on ecosystem functions in space and time. The landscape or regional scale means an area of about 100 km2 up to some 1000 km2. Main problems on this scale are high complexity, structural diversity, ecological heterogeneity and uncertainty in data, in understanding of the process dynamic and by uncertainty in models.

Nesting Genetic Algorithms To Solve A Robust Optimal Experimental Design Problem, Dirk J. W. De Pauw, Peter A. Vanrolleghem

International Congress on Environmental Modelling and Software

When calibrating a (dynamic) model, one is often faced with a lack of information-rich data. Without such data, there is little hope in obtaining accurate parameter estimates. In order to improve the situation, optimal experimental design for parameter estimation (OED-PE) can be employed. The main drawback of the classical OED-PE methodology is that values for the model parameters need to be provided in order to obtain an optimal design. If the values of the model parameters are highly uncertain, robust OED-PE should be preferred, yielding a design which guarantees a certain information content given the parameter uncertainty. This approach adds …

Prescriptive Treatment Optimization Using A Genetic Algorithm: A Tool For Forest Management, John Dewey, Frederick Maier, Walter D. Potter, Donald Nute, H. M. Rauscher, Mark Twery, Peter Knopp, Scott Thomasma

International Congress on Environmental Modelling and Software

This paper describes research on the use of a multiobjective genetic algorithm (GA) to optimize prescriptive treatment plans for forest management. The algorithm is novel, in that (1) the plans generated by the algorithm are highly specific, stating precisely when and where treatments are to be applied; and (2) logical rules and inference engines developed for a decision support system are used to evaluate the fitness of each plan. Fitness is based upon satisfaction of varied and often incompatible goals. The current (generational) GA has been compared in experiments to hill-climbing and simulated annealing algorithms, as well as to a …

New Directions And Challenges In Spatial Dynamic Modelling Of Ecosystem Functions In Heterogeneous Landscapes As Basis For A Better Sustainable Landscape Management, Karl-Otto Wenkel, Ralf Wieland, Wilfried Mirschel

International Congress on Environmental Modelling and Software

The aim of this paper is to discuss scientific challenges and new possibilities for a better modelling of consequences of land use changes in heterogeneous landscapes on ecosystem functions in space and time. The landscape or regional scale means an area of about 100 km2 up to some 1000 km2. Main problems on this scale are high complexity, structural diversity, ecological heterogeneity and uncertainty in data, in understanding of the process dynamic and by uncertainty in models.

Nesting Genetic Algorithms To Solve A Robust Optimal Experimental Design Problem, Dirk J. W. De Pauw, Peter A. Vanrolleghem

International Congress on Environmental Modelling and Software

When calibrating a (dynamic) model, one is often faced with a lack of information-rich data. Without such data, there is little hope in obtaining accurate parameter estimates. In order to improve the situation, optimal experimental design for parameter estimation (OED-PE) can be employed. The main drawback of the classical OED-PE methodology is that values for the model parameters need to be provided in order to obtain an optimal design. If the values of the model parameters are highly uncertain, robust OED-PE should be preferred, yielding a design which guarantees a certain information content given the parameter uncertainty. This approach adds …

A Study Of Genetic Representation Schemes For Scheduling Soft Real-Time Systems, Amit Bugde

Theses and Dissertations

This research presents a hybrid algorithm that combines List Scheduling (LS) with a Genetic Algorithm (GA) for constructing non-preemptive schedules for soft real-time parallel applications represented as directed acyclic graphs (DAGs). The execution time requirements of the applications' tasks are assumed to be stochastic and are represented as probability distribution functions. The performance in terms of schedule lengths for three different genetic representation schemes are evaluated and compared for a number of different DAGs. The approaches presented in this research produce shorter schedules than HLFET, a popular LS approach for all of the sample problems. Of the three genetic representation …

A Platform For Antenna Optimization With Numerical Electromagnetics Code Incorporated With Genetic Algorithms, Timothy L. Pitzer

Theses and Dissertations

This thesis investigation presents a unique incorporation of the Method of Moments (MoM) with a Genetic Algorithm (GA). A GA is used in accord with the Numerical Electromagnetics Code, Version 4 (NEC4) to create and optimize typical wire antenna designs, including single elements and arrays. Design parameters for the antenna are defined and encoded into a chromosome composed of a series of numbers. The cost function associated with the specific antenna of interest is what quantifies improvement and, eventually, optimization. This cost function is created and used by the GA to evaluate the performance of a population of antenna designs. …

A Novel Approach To Phylogenetic Tree Construction Using Stochastic Optimization And Clustering, Ling Qin, Yixin Chen, Yi Pan, Ling Chen

Computer Science Faculty Publications

Background: The problem of inferring the evolutionary history and constructing the phylogenetic tree with high performance has become one of the major problems in computational biology.

Results: A new phylogenetic tree construction method from a given set of objects (proteins, species, etc.) is presented. As an extension of ant colony optimization, this method proposes an adaptive phylogenetic clustering algorithm based on a digraph to find a tree structure that defines the ancestral relationships among the given objects.

Conclusion: Our phylogenetic tree construction method is tested to compare its results with that of the genetic algorithm (GA). Experimental results show that …

Faculty Scheduling Using Genetic Algorithms, Kevin Soule

Theses

The problem of developing a class schedule for a faculty has been proven to be NP-complete. Therefore when the schedule is large enough, finding just one feasible solution can be impossible for any direct search algorithm within a reasonable time. This project is geared toward investigating the possibility of using genetic-based algorithms to solve faculty scheduling problems of 100 courses or larger quickly. Multiple versions of genetic algorithms and heuristics are tested. Many parameter levels for these algorithms are optimized for fastest convergence.

Ternary Quantum Logic, Normen Giesecke

Dissertations and Theses

The application of Moore's Law would not be feasible by using the computing systems fabrication principles that are prevalent today. Fundamental changes in the field of computing are needed to keep Moore's Law operational. Different quantum technologies are available to take the advancement of computing into the future. Logic in quantum technology uses gates that are very different from those used in contemporary technology. Limiting itself to reversible operations, this thesis presents different methods to realize these logic gates. Two methods using Generalized Ternary Gates and Muthukrishnan Stroud Gates are presented for synthesis of ternary logic gates. Realizations of well-known …

Metaheuristics And Combinatorial Optimization Problems, Gerald Skidmore

Theses

This thesis will use the traveling salesman problem (TSP) as a tool to help present and investigate several new techniques that improve the overall performance of genetic algorithms (GA). Improvements include a new parent selection algorithm, harem select, that outperforms all other parent selection algorithms tested, some by up to 600%. Other techniques investigated include population seeding, random restart, heuristic crossovers, and hybrid genetic algorithms, all of which posted improvements in the range of 1% up to 1100%. Also studied will be a new algorithm, GRASP, that is just starting to enjoy a lot of interest in the research community …

Automatic Colonic Polyp Detection Using Multiobjective Evolutionary Techniques, Jiang Li, Adam Huang, Jianhua Yao, Ingmar Bitter, Nicholas Petrick, Ronald M. Summers, Perry J. Pickhardt, J. Richard Choi

Electrical & Computer Engineering Faculty Publications

Colonie polyps appear like elliptical protrusions on the inner wall of the colon. Curvature based features for colonie polyp detection have proved to be successful in several computer-aided diagnostic CT colonography (CTC) systems. Some simple thresholds are set for those features for creating initial polyp candidates, sophisticated classification scheme are then applied on these polyp candidates to reduce false positives. There are two objective functions, the number of missed polyps and false positive rate, that need to be minimized when setting those thresholds. These two objectives conflict and it is usually difficult to optimize them both by a gradient search. …

Genetic Music, Ryan Becker

Theses

Algorithmic music composition has long been an active area of research in computer science, but the need for a human element only recently began to be more widely acknowledged. Interactive Evolutionary Computing (IEC), made popular by Karl Sims, effectively solves many high dimension problems, like music composition, involving creative and subjective elements. This work applies several Genetic Algorithm (GA) and Genetic Programming (GP) approaches, inspired by Karl Sims, to algorithmic music composition. The implementation of these IEC algorithms is described and their effectiveness compared.