Carnegie Mellon University |Machine Learning Department, School of Computer Science
Course: Algorithms in Nature
Special Thanks to Professor Ziv Bar Joseph
Research - Project 1 |
Researched on design solutions optimized for energy efficiency
Title: MAXIMIZING PRESERVED SUNLIGHT IN PNEUMATIC SOLAR PANELS
APPLICATION | Building Facade Components
APPROACH | OPTIMIZATION - GENETIC ALGORITHM
Fereshteh Shahmiri, Alan Shteyman
Research - Project 2 |
Researched on one important part of DATA MINING PROBLEMS | FINDING DENSE MODULES OR CLUSTERS IN A GRAPH
Title: GENETIC ALGORITHM TO CLUSTER GRAPHS
Finding dense module or clusters in a graph is an important part of many data mining problems. One popular definition of a 'module' is a set of nodes that have many more within-module connections (i.e. connections between nodes in the same modules) than expected by chance. In 2002, Newman proposed an objective function, called modularity, that characterized the quality of clustering C of a graph G = ( V, E)
The Goal is to find the clustering C that MAXIMIZE Newman Function. In general the clustering C can have any number of modules( from 1 to n, where n is the number of nodes in the graph) but all nodes must be assigned to exactly one module.
PROJECT DEFINITION |
Writing a genetic program to cluster an input graph into modules that optimized the Newman objective function, I used here at most 5 clusters.
EXPECTED OUTPUT |
Program should output the OPTIMAL MODULARITY found as well as the CLUSTERING corresponding to the optimal modularity.
POINTS NEED TO BE CONSIDERED |
For this problem, a CLUSTERING is the analog of an 'individual'.
The 'FITNESS FUNCTION' is equivalent to the 'MODULARITY FUNCTION'.
The main idea behind the solution and using the operations like MUTATION and CROSS-OVER, etc. and the way of ensuring that how these operation always produce a valid clustering. (e.g. how it is ensured that a node was not assigned to multiple clusters?)
RELEVANT RESEARCH ON GENETIC ALGORITHM |
Part 1 |
PROBABILITY OF PRESERVING THE SCHEMA FROM DESTRUCTION OCCURING BY SOME OPERATIONS LIKE MUTATION
DESCRIPTION | In genetic algorithms, schema is a template that defines a set of possible strings. A String destroys the schema if the string is not a possible string defined by the schema.Research structured on understanding how the probability that a schema will not be destroyed by operations like mutation that occurs with specific number for its probability, is calculated?
Part 2 |
CALCULATION OF AVERAGE FITNESS OF THE EXPLAINED SCHEMA
RESEARCH - PROJECT 3 |
Based on specific given data, how first two principal components that would be found using the PCA algorithm have to be drawn?
How is the reconstruction error if the choice is ONE component? Or TWO components?
Title : DIMENSIONALITY REDUCTION
Introduction to biology
Introduction to distributed computing
Non-negative matrix factorization
Optimization and Search
Maximum Independent Set (MIS)
MIS and SOP
Slime Mold Design
Network Growth Models