SOME FURTHER EXPERIMENTS WITH THE GENETIC ALGORITHM FOR THE QUADRATIC ASSIGNMENT PROBLEM
2015
In this paper, some further experiments with the genetic algorithm (GA) for the quadratic assignment problem (QAP) are described. We propose to use a particle-swarm-optimization-based approach for tuning the values of the parameters of the genetic algorithm for solving the QAP. The resulting combined self-adaptive swarm optimization- genetic algorithm enables to efficiently auto-configure the control parameters for GA — which leads to excellent quality solutions, especially for the real-life like (structured) QAP instances.
Keywords:
- Combinatorial optimization
- Machine learning
- Mathematical optimization
- Artificial intelligence
- Population-based incremental learning
- Genetic algorithm
- Assignment problem
- Generalized assignment problem
- Quadratic assignment problem
- Meta-optimization
- Swarm behaviour
- Mathematics
- Algorithm
- control parameters
- Computer science
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