An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Hierarchical Genetic Algorithm Using Multiple Models for Optimization
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Expert Systems with Applications: An International Journal
A novel approach to adaptive isolation in evolution strategies
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Multi-robot path planning using co-evolutionary genetic programming
Expert Systems with Applications: An International Journal
Modular symbiotic adaptive neuro evolution for high dimensionality classificatory problems
Intelligent Decision Technologies
Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms
International Journal of Intelligent Information Technologies
Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks
Journal of Information Technology Research
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We propose the use of a hierarchical genetic algorithm (GA) for optimisation in complex landscapes. While the slave GA tries to find the local optima in the restricted fitness landscape of low complexity, the master GA tries to identify interesting regions in the entire landscape. The slave GA is a conventional GA with high convergence. The master GA is more exploratory in nature. This GA clusters the fitness landscape with each cluster in control of a slave GA. The number of clusters decreases with time to get global characteristics. The novelty of the suggested approach lies in the trade-off between the search for global optima and convergence to local optima that can be controlled between the two GAs. We tested the algorithm and observed that the approach exceeds conventional GA as well as particle swarm optimisation in complex landscapes.