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 adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The ARGOT strategy: adaptive representation genetic optimizer technique
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Adapting operator probabilities in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Varying the probability of mutation in the genetic algorithm
Proceedings of the third international conference on Genetic algorithms
GENITOR II.: a distributed genetic algorithm
Journal of Experimental & Theoretical Artificial Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computer
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Binomially Distributed Populations for Modelling GAs
Proceedings of the 5th International Conference on Genetic Algorithms
Query Rewriting and Search in CROQUE
ADBIS '99 Proceedings of the Third East European Conference on Advances in Databases and Information Systems
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
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Genetic Algorithms are efficient and robust search methods that are being employed in a plethora of applications with extremely large search spaces. The directed search mechanism employed in Genetic Algorithms performs a simultaneous and balanced, exploration of new regions in the search space and exploitation of already discovered regions.This paper introduces the notion of fitness moments for analyzing the working of Genetic Algorithms (GAs). We show that the fitness moments in any generation may be predicted from those of the initial population. Since a knowledge of the fitness moments allows us to estimate the fitness distribution of strings, this approach provides for a method of characterizing the dynamics of GAs. In particular the average fitness and fitness variance of the population in any generation may be predicted.We introduce the technique of fitness-based disruption of solutions for improving the performance of GAs. Using fitness moments, we demonstrate the advantages of using fitness-based disruption. We also present experimental results comparing the performance of a standard GA and GAs (CDGA and AGA) that incorporate the principle of fitness-based disruption. The experimental evidence clearly demonstrates the power of fitness based disruption.