## Abstract

This chapter presents a rigorous Design of Experiments (DOE) approach for determining whether a problem characteristic affects the performance of a heuristic. Specifically, it reports a study on the effect of the cost matrix standard deviation of symmetric Travelling Salesman Problem (TSP) instances on the performance of Ant Colony Optimisation (ACO) heuristics. Results demonstrate that for a given instance size, an increase in the standard deviation of the cost matrix of instances results in all increase in the difficulty of the instances. This implies that for ACO, it is insufficient to report results on problems classified only by problem size, as has been commonly done in most ACO research to date. Some description of the cost matrix distribution is also required when attempting to explain and predict the performance of these heuristics on the TSP. The study should serve as a template for similar investigations with other problems and other heuristics.

Original language | English |
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Title of host publication | RECENT ADVANCES IN EVOLUTIONARY COMPUTATION FOR COMBINATORIAL OPTIMIZATION |

Editors | C Cotta, J VanHemert |

Place of Publication | NEW YORK |

Publisher | Springer |

Pages | 21-35 |

Number of pages | 15 |

ISBN (Print) | 978-3-540-70806-3 |

Publication status | Published - 2008 |

Event | 7th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2007) - Valencia Duration: 11 Apr 2007 → 13 Apr 2007 |

### Conference

Conference | 7th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2007) |
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City | Valencia |

Period | 11/04/07 → 13/04/07 |

## Keywords

- Design of Experiments
- Nest Hierarchical Design
- Problem Difficulty
- Ant Colony Optimisation