Feature selection is the process of refining input data by removing irrelevant and/or redundant features. Feature selection deals with selection of a ...read more »
A Comparative Study of Ant Colony Optimization and Genetic Algorithms on a TSP
Population-based stochastic search algorithms have been applied on several NP-hard combinatorial problems. Due to their parallel nature, multiple solutions are evolved at the same time. Such evolutionary algorithms have their strengths in different problem areas thus the “no free launch” theory holds for algorithms in this domain. This work seeks to compare performance of a swarm intelligence algorithm (Ant Colony Optimization (ACO)) and a Genetic Algorithm on a Traveling Salesman Problem (TSP). It was discovered that, ACO produced better results within a shorter time frame as opposed to GA. GA performed best for datasets with minimal number of nodes however needed more generations to produce results that are competitive to ACO for larger datasets. Statistical test (t-test) for the observed difference is significant at 90% confidence level and insignificant at 95% confidence level.
Entire system was developed in java See https://www.github.com/aawuley/evolutionary-computation
In my spare time, i enjoy working on some NP complete problems. The latest ones include(but not limited to) feature selection, computer ...read more »
From My Blog
Genetic Algorithm and Ant Colony Optimization was tested on a TSP. According to the results, ACO outperformed GA on the TSP problem. ...
I am the community owner and administrator of the "Evolutionary Algorithms" group on g+
I am the community owner and administrator of the "Genetic Programming" group on g+