Carnegie Mellon University Machine Learning Department, School of Computer Science
Course: Algorithms in Nature
Special Thanks to Professor Ziv Bar Joseph
Fall 2013
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
Fereshteh Shahmiri
Project and Code Implementation Link Here
DESCRIPTION 
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 withinmodule 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 CROSSOVER, 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
LECTURE TOPICS:

Introduction to biology

Introduction to distributed computing

Regression

Neural Networks

Dimensionality Reduction

Nonnegative matrix factorization

Optimization and Search

Genetic Algorithms

Ant Algorithms

Maximum Independent Set (MIS)

Lateral inhibition

MIS and SOP

Steiner Trees

Slime Mold Design

Network Growth Models

Pruning Algorithms

Robustness