Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Adaptive discretization for probabilistic model building genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
iECGA: integer extended compact genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Real-coded ECGA for economic dispatch
Proceedings of the 9th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
This paper extends an adaptive discretization method, Split-on-Demand (SoD), to be capable of handling multidimensional continuous search spaces. The proposed extension is called multidimensional Split-on-Demand (mSoD), which considers multiple dimensions of the search space as a whole instead of independently discretizing each dimension as SoD does. In this study, we integrate mSoD and SoD with the extended compact genetic algorithm (ECGA) to numerically examine the effectiveness and performance of mSoD and SoD on the problems with and without linkage among dimensions of the search space. The experimental results indicate that mSoD outperforms SoD on both types of the test problems and that mSoD can offer better scalability, stability, and accuracy. The behavior of mSoD is discussed, followed by the potential future work.