Physical Sciences and Mathematics Commons^™

Co-Optimization Of A Robot's Body And Brain Via Evolution And Reinforcement Learning, Jack Felag

Graduate College Dissertations and Theses

Agents are often trained to perform a task via optimization algorithms. One class of algorithms used is evolution, which is ``survival of the fitness'' used to pick the best agents for the objective, and slowly changing the best over time to find a good solution. Evolution, or evolutionary algorithms, have been commonly used to automatically select for a better body of the agent, which can outperform hand-designed models. Another class of algorithms used is reinforcement learning. Through this strategy, agents learn from prior experiences in order to maximize some reward. Generally, this reward is how close the objective is to …

Exploring The Modularity And Structure Of Robots Evolved In Multiple Environments, Collin Cappelle

Graduate College Dissertations and Theses

Traditional techniques for the design of robots require human engineers to plan every aspect of the system, from body to controller. In contrast, the field of evolu- tionary robotics uses evolutionary algorithms to create optimized morphologies and neural controllers with minimal human intervention. In order to expand the capability of an evolved agent, it must be exposed to a variety of conditions and environments.

This thesis investigates the design and benefits of virtual robots which can reflect the structure and modularity in the world around them. I show that when a robot’s morphology and controller enable it to perceive each …

Hexarray: A Novel Self-Reconfigurable Hardware System, Fady Hussein

Boise State University Theses and Dissertations

Evolvable hardware (EHW) is a powerful autonomous system for adapting and finding solutions within a changing environment. EHW consists of two main components: a reconfigurable hardware core and an evolutionary algorithm. The majority of prior research focuses on improving either the reconfigurable hardware or the evolutionary algorithm in place, but not both. Thus, current implementations suffer from being application oriented and having slow reconfiguration times, low efficiencies, and less routing flexibility. In this work, a novel evolvable hardware platform is proposed that combines a novel reconfigurable hardware core and a novel evolutionary algorithm.

The proposed reconfigurable hardware core is a …

On The Role Of Genetic Algorithms In The Pattern Recognition Task Of Classification, Isaac Ben Sherman

Masters Theses

In this dissertation we ask, formulate an apparatus for answering, and answer the following three questions: Where do Genetic Algorithms fit in the greater scheme of pattern recognition? Given primitive mechanics, can Genetic Algorithms match or exceed the performance of theoretically-based methods? Can we build a generic universal Genetic Algorithm for classification? To answer these questions, we develop a genetic algorithm which optimizes MATLAB classifiers and a variable length genetic algorithm which does classification based entirely on boolean logic. We test these algorithms on disparate datasets rooted in cellular biology, music theory, and medicine. We then get results from these …

Hyper-Heuristics For The Automated Design Of Black-Box Search Algorithms, Matthew Allen Martin

Masters Theses

"Within the field of Black-Box Search Algorithms (BBSAs), there is a focus on improving algorithm performance over increasingly diversified problem classes. However, these general purpose problem solvers have no guarantee to perform well on an arbitrary problem class that a practitioner needs to solve. The problem classes that the research in this thesis most applies to are difficult problems that are going to be solved multiple times. BBSAs tailored to one of these problem class can be expected to significantly outperform the more general purpose problem solvers, including canonical Evolutionary Algorithms (EAs). The first paper in this thesis explores a …

Darwin: A Ground Truth Agnostic Captcha Generator Using Evolutionary Algorithm, Eric Y. Chen, Lin-Shung Huang, Ole J. Mengshoel, Jason D. Lohn

Ole J Mengshoel

Feedback Control For Multi-Modal Optimization Using Genetic Algorithms, Jun Shi, Ole J. Mengshoel, Dipan K. Pal

Ole J Mengshoel

Evolving Decision Trees For The Categorization Of Software, Jasenko Hosic

Masters Theses

"Current manual techniques of static reverse engineering are inefficient at providing semantic program understanding. An automated method to categorize applications was developed in order to quickly determine pertinent characteristics. Prior work in this area has had some success, but a major strength of the approach detailed in this thesis is that it produces heuristics that can be reused for quick analysis of new data. The method relies on a genetic programming algorithm to evolve decision trees which can be used to categorize software. The terminals, or leaf nodes, within the trees each contain values based on selected features from one …

Adaptive Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severinio Galan, Antonio De Dios

Ole J Mengshoel

A Novel Mating Approach For Genetic Algorithms, Severino Galan, Ole J. Mengshoel, Rafael Pinter

Ole J Mengshoel

Age-Layered Expectation Maximization For Parameter Learning In Bayesian Networks, Avneesh Saluja, Priya Sundararajan, Ole J. Mengshoel

Ole J Mengshoel

Bringing To Life An Ancient Urban Center At Monte Albán, Mexico: Exploiting The Synergy Between The Micro, Meso, And Macro Levels In A Complex System, Thaer W. Jayyousi

Wayne State University Dissertations

In this dissertation, agent-based models of emergent ancient urban centers were constructed through the use of techniques from computational intelligence, agent-based modeling, complex systems, and data-mining of existing archaeological data from the prehistoric urban center, Monte Albán. This real world application was selected because of its importance in understanding the emergence of modern economic and political systems. Specifically, Cultural Algorithms was used to evolve models of early Monte Alban, models that can then be compared with existing models of ancient and modern urban centers.

Features of a complex system were used to help interpret the archaeological data. The analysis went …

Using Secondary Fitness To Break Ties In A Genetic Algorithm For The One-Dimensional Bin-Packing Problem, Justin W. Benjamin

Culminating Projects in Computer Science and Information Technology

The one-dimensional bin-packing problem (BPP) is a well known problem in the realm of operations research. In the BPP, objects with pre-defined "weights" are packed into bins, each having a maximum weight capacity. The goal is to minimize the number of bins needed to hold objects.

In a Genetic Algorithm for this problem, an individual candidate solution consists of a permutation of object~ representing their order of placement. A heuristic is then used to place objects in bins according to their specified order. The heuristic chosen is generally First Fit, Best Fit, or Worst Fit. The number of bins needed …

Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severino F. Galan

Ole J Mengshoel

Constraint Handling Using Tournament Selection: Abductive Inference In Partly Deterministic Bayesian Network, Severino F. Galan, Ole J. Mengshoel

Ole J Mengshoel

Constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal …

Evolutionary Methodology For Optimization Of Image Transforms Subject To Quantization Noise, Michael Ray Peterson

Browse all Theses and Dissertations

Lossy image compression algorithms sacrifice perfect imagereconstruction in favor of decreased storage requirements. Modelossy compression schemes, such as JPEG2000, rely upon the discrete wavelet transform (DWT) to achieve high levels of compression while minimizing the loss of information for image reconstruction. Some compression applications require higher levels of compression than those achieved through application of the DWT and entropy coding. In such lossy systems, quantization provides high compression rates at the cost of increased distortion. Unfortunately, as the amount of quantization increases, the performance of the DWT for accurate image reconstruction deteriorates. Previous research demonstrates that a genetic algorithm can …

The Crowding Approach To Niching In Genetic Algorithms, Ole J. Mengshoel, David E. Goldberg

Ole J Mengshoel

A wide range of niching techniques have been investigated in evolutionary and genetic algorithms. In this article, we focus on niching using crowding techniques in the context of what we call local tournament algorithms. In addition to deterministic and probabilistic crowding, the family of local tournament algorithms includes the Metropolis algorithm, simulated annealing, restricted tournament selection, and parallel recombinative simulated annealing. We describe an algorithmic and analytical framework which is applicable to a wide range of crowding algorithms. As an example of utilizing this framework, we present and analyze the probabilistic crowding niching algorithm. Like the closely related deterministic crowding …

An Analysis Of Neutral Drift's Effect On The Evolution Of A Ctrnn Locomotion Controller With Noisy Fitness Evaluation, Gregory Robert Kramer

Browse all Theses and Dissertations

This dissertation focuses on the evolution of Continuous Time Recurrent Neural Networks (CTRNNs) as controllers for control systems. Existing research suggests that the process of neutral drift can greatly benefit evolution for problems whose fitness landscapes contain large-scale neutral networks. CTRNNs are known to be highly degenerate, providing a possible source of large-scale landscape neutrality, and existing research suggests that neutral drift benefits the evolution of simple CTRNNs. However, there has been no in-depth examination of the effects of neutral drift on complex CTRNN controllers, especially in the presence of noisy fitness evaluation. To address this problem, this dissertation presents …

Pattern Recognition Via Machine Learning With Genetic Decision-Programming, Carl C. Hoff

Browse all Theses and Dissertations

In the intersection of pattern recognition, machine learning, and evolutionary computation is a new search technique by which computers might program themselves. That technique is called genetic decision-programming. A computer can gain the ability to distinguish among the things that it needs to recognize by using genetic decision-programming for pattern discovery and concept learning. Those patterns and concepts can be easily encoded in the spines of a decision program (tree or diagram). A spine consists of two parts: (1) the test-outcome pairs along a path from the program's root to any of its leaves and (2) the conclusion in that …

Probabilistic Crowding: Deterministic Crowding With Probabilistic Replacement, Ole J. Mengshoel, David E. Goldberg

Ole J Mengshoel

This paper presents a novel niching algorithm, probabilistic crowding. Like its predecessor deterministic crowding, probabilistic crowding is fast, simple, and requires no parameters beyond that of the classical GA. In probabilistic crowding, subpopulations are maintained reliably, and we analyze and predict how this maintenance takes place.

This paper also identifies probabilistic crowding as a member of a family of algorithms, which we call integrated tournament algorithms. Integrated tournament algorithms also include deterministic crowding, restricted tournament selection, elitist recombination, parallel recombinative simulated annealing, the Metropolis algorithm, and simulated annealing.