Abstract
The purpose of this work is to compare the performance of a scatter search (SS) implementation and an implementation of a genetic algorithm (GA) in the context of searching for optimal solutions to permutation problems. Scatter search and genetic algorithms are members of the evolutionary computation family. That is, they are both based on maintaining a population of solutions for the purpose of generating new trial solutions. Our computational experiments with four well-known permutation problems reveal that in general a GA with local search outperforms one without it. Using the same problem instances, we observed that our specific scatter search implementation found solutions of a higher average quality earlier during the search than the GA variants.
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Martí, R., Laguna, M., Campos, V. (2005). Scatter Search vs. Genetic Algorithms. In: Sharda, R., Voß, S., Rego, C., Alidaee, B. (eds) Metaheuristic Optimization via Memory and Evolution. Operations Research/Computer Science Interfaces Series, vol 30. Springer, Boston, MA. https://doi.org/10.1007/0-387-23667-8_12
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DOI: https://doi.org/10.1007/0-387-23667-8_12
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