Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Binomially Distributed Populations for Modelling GAs
Proceedings of the 5th International Conference on Genetic Algorithms
Global Convergence of Genetic Algorithms: A Markov Chain Analysis
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Self-Adaptive Genetic Algorithm for Numeric Functions
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An Adaptive Parallel Genetic Algorithm for VLSI-Layout Optimization
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Discovering Numeric Association Rules via Evolutionary Algorithm
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Application of a breeder genetic algorithm for finite impulse filter optimization
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: FEA 2002
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
The parameter-less genetic algorithm in practice
Information Sciences—Informatics and Computer Science: An International Journal
Free search: a comparative analysis
Information Sciences—Informatics and Computer Science: An International Journal
Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
Evolutionary Computation
Evolutionary approach for mining association rules on dynamic databases
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
Convergence analysis of canonical genetic algorithms
IEEE Transactions on Neural Networks
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
A genetic algorithm calibration method based on convergence due to genetic drift
Information Sciences: an International Journal
International Journal of Hybrid Intelligent Systems
Data gravitation based classification
Information Sciences: an International Journal
Self-organizing genetic algorithm based tuning of PID controllers
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Knowledge management technology for organized crime risk assessment
Information Systems Frontiers
BGSA: binary gravitational search algorithm
Natural Computing: an international journal
On-the-fly calibrating strategies for evolutionary algorithms
Information Sciences: an International Journal
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
DAMS: distributed adaptive metaheuristic selection
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Structural design of the danger model immune algorithm
Information Sciences: an International Journal
Gravitation based classification
Information Sciences: an International Journal
When I Seem More Important than T in IT: The Case of Police Intelligence
International Journal of Strategic Information Technology and Applications
A framework for evolutionary algorithms based on Charles Sanders Peirce's evolutionary semiotics
Information Sciences: an International Journal
An analysis of the migration rates for biogeography-based optimization
Information Sciences: an International Journal
Structural and Multidisciplinary Optimization
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Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.