ACO Implementation for Sequence Alignment with Genetic Algorithms

TL;DR

This paper explores the application of Ant Colony Optimization combined with genetic algorithms to improve sequence alignment, demonstrating its potential despite higher computational costs compared to traditional methods.

Contribution

It introduces a novel hybrid approach using ACO and genetic algorithms for sequence alignment parameter optimization.

Findings

ACO can be applied to sequence alignment.

The method finds approximate optimal parameters for different string lengths.

ACO shows promise despite higher computational expense.

Abstract

In this paper, we implement Ant Colony Optimization (ACO) for sequence alignment. ACO is a meta-heuristic recently developed for nearest neighbor approximations in large, NP-hard search spaces. Here we use a genetic algorithm approach to evolve the best parameters for an ACO designed to align two sequences. We then used the best parameters found to interpolate approximate optimal parameters for a given string length within a range. The basis of our comparison is the alignment given by the Needleman-Wunsch algorithm. We found that ACO can indeed be applied to sequence alignment. While it is computationally expensive compared to other equivalent algorithms, it is a promising algorithm that can be readily applied to a variety of other biological problems.