Tyler Derr – Past Projects
Projects at Michigan State University
Evolving Multi-Layer Markov Network Brains Using Adaptive Complexification
Evolving Quick Learners: Novel Initialization Strategies for
Markov Network Brains
Abstract:
Projects at The Pennsylvania State University
Master's Thesis:
A Clustering Approach to the Bounded Diameter Minimum Spanning Tree Problem Using Ants
Download Thesis(pdf)
Master's Advisor: Dr. Thang N. Bui at
Penn State Harrisburg
Abstract:
The bounded diameter minimum spanning tree problem is the problem of
finding a minimum cost spanning tree of a graph such that the number
of edges along the longest path in the tree is at most d. This problem
is well known to be NP-hard. We present an ant-based algorithm for
this problem in which we use two species of ants. The first species is
used to discover clusters in the vertex set and then a bounded
diameter spanning tree (BDST) is created within each cluster. The
second species is then used to connect the cluster BDSTs together
building a bounded diameter spanning tree for the whole graph. This
tree is then locally optimized yielding a solution to the overall
problem. Experimental tests have been conducted on two types of
complete graphs, 135 Euclidean and 120 non-Euclidean, totalling 255
graphs. Each Euclidean graph consists of a set of vertices randomly
placed in the unit square and the edge cost between two vertices is
the Euclidean distance between them. The non-Euclidean graphs are
structured such that all of the edge weights in the graph have been
randomly selected from 0.01,0.99. For the Euclidean graphs the
results show that our algorithm achieves solutions close to the best
known for most of the instances. However, on the non-Euclidean graphs
our algorithm has obtained new best known solutions for the majority
of the graphs and has come very close to the best known in the
others.
Yue Lab
A Supervised Learning Approach to the Prediction of Hi-C Data
Download
Poster(pdf)
Supervisor: Dr. Feng Yue at Penn State College of
Medicine
Developed a machine learning framework for the prediction of
3-dimensional genome organization data, more specifically
high-throughput chromatin conformation capture (Hi-C).
The framework further allowed cross-tissue/cell prediction helping
to gain insights into tissue-specific gene expression while
discovering novel biological insights into which genomic/epidenomic
features are critical towards chromatin interactions.
Other
Parallel Distributed Genetic Algorithm for Feature Selection
Summary:
Micromouse for the IEEE Region 2 Student Activites Conference
Software Verification and Security Analysis by Modeling System Specifications
Voice-to-Braille Translation System
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