Problem Statement and Motivation
Key Achievements and Future Goals
Investigators: Peter Nelson, CS;
Li, CS; Chi Zhou, Motorola Inc.
Prime Grant Support: Physical Realization Research Center of Motorola Labs
Real world data mining tasks
: large data set, high dimensional feature
linear form of hidden knowledge; in need of effective
Gene Expression Programming
(GEP): a new evolutionary
computation technique for the creation of computer programs; capable
of producing solutions of any possible form.
: applying and enhancing GEP algorithm to fulfill
complex data mining tasks.
Have finished the initial implementation of the proposed approaches.
Preliminary testing has demonstrated the feasibility and effectiveness
of the implemented methods: constant creation methods have
achieved significant improvement in the fitness of the best solutions;
dynamic substructure library helps identify meaningful building blocks
to incrementally form the final solution following a faster fitness
Future work include investigation for parametric constants, exploration
of higher level emergent structures, and comprehensive benchmark
: improving the problem solving ability of the GEP algorithm
by preserving and utilizing the self
emergence of structures during its
Constant Creation Methods for GEP
: local optimization of constant
coefficients given the evolved solution structures to speed up the
A new hierarchical genotype representation
: natural hierarchy in
forming the solution and more protective genetic operation for
Dynamic substructure library
: defining and reusing self
substructures in the evolutionary process.
Figure 1. Representations of solutions in GEP