Various applications of spectral techniques for enhancing graph bisection in genetic algorithms are investigated. Several enhancements to a genetic algorithm for graph bisection a...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Estimation of Distribution Algorithms (EDAs) are a class of evolutionary algorithms that use machine learning techniques to solve optimization problems. Machine learning is used t...
The representation used by a learning algorithm introduces a bias which is more or less well-suited to any given learning problem. It is well known that, across all possible probl...
Heuristic Algorithms (HA) are very widely used to tackle practical problems in operations research. They are simple, easy to understand and inspire confidence. Many of these HAs a...
In this paper, we present an empirical comparison of some Differential Evolution variants to solve global optimization problems. The aim is to identify which one of them is more s...
To help unravel the structure of the universe, astronomers have developed systems which observe large clusters of objects at the same time. One such system is the 2-degree field s...
We describe a Markov chain Bayesian classification tool, SCS, that can perform data-driven classification of proteins and protein segments. Training data for interesting classific...
Timothy Meekhof, Gary W. Daughdrill, Robert B. Hec...