Name
Title | ||
|---|---|---|
Iterative Cartesian Genetic Programming: Creating General Algorithms for Solving Travelling Salesman Problems. |
Abstract | ||
|---|---|---|
Evolutionary algorithms have been widely used to optimise or design search algorithms, however, very few have considered evolving iterative algorithms. In this paper, we introduce a novel extension to Cartesian Genetic Programming that allows it to encode iterative algorithms. We apply this technique to the Traveling Salesman Problem to produce human-readable solvers which can be then be independently implemented. Our experimental results demonstrate that the evolved solvers scale well to much larger TSP instances than those used for training. |
Year | Venue | Field |
|---|---|---|
2016 | EuroGP | ENCODE,Search algorithm,Evolutionary algorithm,Computer science,Algorithm,Theoretical computer science,Cartesian genetic programming,Travelling salesman problem,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patricia Ryser-Welch | 1 | 3 | 1.40 |
| Julian F. Miller | 2 | 2011 | 228.72 |
| Jerry Swan | 3 | 196 | 19.47 |
| Martin A. Trefzer | 4 | 52 | 12.24 |