Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An analysis of reproduction and crossover in a binary-coded genetic algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An analysis of the effects of selection in genetic algorithms
An analysis of the effects of selection in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Analysis of genetic algorithms from a global random search method perspective with techniques for algorithmic improvement
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Noisy Environments
Machine Learning
How Genetic Algorithms Work: A Critical Look at Implicit Parallelism
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Using Genetic Algorithms with Small Populations
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
The Theory of Virtual Alphabets
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon
Fundamenta Informaticae
Bio-Inspired Techniques for Resources State Prediction in Large Scale Distributed Systems
International Journal of Distributed Systems and Technologies
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Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.