Genetic Algorithm Performance with Different Selection Strategies in Solving TSP

By Noraini Mohd Razali, John Geraghty. 2011

478 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f5d7e26f21603c13dfe8e412106127039a547edb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Stefano Forli
2. Diogo Santos-Martins
3. Andreas F. Tillack
4. Michel F. Sanner
5. Leonardo Solis-Vasquez
6. Ahmad Hassanat
7. Andreas Koch
8. Khalid Almohammadi

Citations in the corpus

• Choosing Mutation and Crossover Ratios for Genetic Algorithms - A Review with a New Dynamic Approach (2019, background): “(a) Tournament Selection: This is arguably the most common selection technique in GA because of its efficiency and ease in implementation [33].”
• Review of Deep Learning Algorithms and Architectures (2019, unknown): “, elite, roulette, rank, tournament [79].”
• Accelerating AutoDock4 with GPUs and Gradient-Based Local Search (2019, background): “Moreover, the major advantage of tournament selection is the low computational effort, especially if implemented in parallel (30), which according the previous studies (26, 27) results in faster executions than those of proportional selection.”