Abstract
Self-adaptive mechanisms for the identification of the most suitable variation operator in Evolutionary meta-heuristics rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel Adaptive Operator Selection mechanism which uses a set of four Fitness Landscape Analysis techniques and an online learning algorithm, Dynamic Weighted Majority, to provide more detailed informations about the search space in order to better determine the most suitable crossover operator on a set of Capacitated Arc Routing Problem (CARP) instances. Extensive comparison with a state of the art approach has proved that this technique is able to produce comparable results on the set of benchmark problems.
| Original language | English |
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| Title of host publication | Simulated Evolution and Learning: 10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, Proceedings |
| Editors | Grant DICK, Will N. BROWNE, Peter WHIGHAM, Mengjie ZHANG, Thu Bui LAM, Hisao ISHIBUCHI, Yaochu JIN, Xiaodong LI, Yuhui SHI, Pramod SINGH, Kay Chen TAN, Ke TANG |
| Publisher | Springer, Cham |
| Pages | 359-370 |
| Number of pages | 12 |
| ISBN (Electronic) | 9783319135632 |
| ISBN (Print) | 9783319135625 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Publisher | Springer, Cham |
| Volume | 8886 |
| ISSN (Print) | 0302-9743 |
Bibliographical note
Publisher Copyright:© Springer International Publishing Switzerland 2014.
Keywords
- Crossover Operator
- Memetic Algorithm
- Concept Drift
- Dynamic Selection
- Credit Assignment