Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Fundamentals of digital image processing
Fundamentals of digital image processing
Global optimization
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Model-based image interpretation using genetic algorithms
Image and Vision Computing - Special issue: BMVC 1991
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Pattern Recognition Letters
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Pattern Recognition Letters - Special issue on genetic algorithms
Genetic optimisation of the image feature extraction process
Pattern Recognition Letters
Application of neural networks and genetic algorithms in the classification of endothelial cells
Pattern Recognition Letters - special issue on pattern recognition in practice V
Pattern Recognition Letters - special issue on pattern recognition in practice V
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Geometric Primitive Extraction Using a Genetic Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithm for affine point pattern matching
Pattern Recognition Letters
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
A parallel genetic algorithm for performance-driven VLSI routing
IEEE Transactions on Evolutionary Computation
A MS-GS VQ codebook design for wireless image communication usinggenetic algorithms
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Genetic-based search for error-correcting graph isomorphism
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A genetic algorithm approach to Chinese handwriting normalization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers and Industrial Engineering
Clustering-based hierarchical genetic algorithm for complex fitness landscapes
International Journal of Intelligent Systems Technologies and Applications
A hierarchical distributed evolutionary algorithm to TSP
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms
International Journal of Intelligent Information Technologies
A robust evolutionary algorithm for the recovery of rational Gielis curves
Pattern Recognition
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In this paper we propose a new approach in genetic algorithm called distributed hierarchical genetic algorithm (DHGA) for optimization and pattern matching. It is eventually a hybrid technique combining the advantages of both distributed and hierarchical processes in exploring the search space. The search is initially distributed over the space and then in each subspace the algorithm works in a hierarchical way. The entire space is essentially partitioned into a number of subspaces depending on the dimensionality of the space. This is done in order to spread the search process more evenly over the whole space. In each subspace the genetic algorithm is employed for searching and the search process advances from one hypercube to a neighboring hypercube hierarchically depending on the convergence status of the population and the solution obtained so far. The dimension of the hypercube and the resolution of the search space are altered with iterations. Thus the search process passes through variable resolution (coarse-to-fine) search space. Both analytical and empirical studies have been carried out to evaluate the performance between DHGA and distributed conventional GA (DCGA) for different function optimization problems. Further, the performance of the algorithms is demonstrated on problems like pattern matching and object matching with edge map.