Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
An introduction to genetic algorithms
An introduction to genetic algorithms
niGAVaPS — outbreeding in genetic algorithms
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
Adaptive Parameterization of Evolutionary Algorithms and Chaotic Populations
Advances in Computational Intelligence and Learning: Methods and Applications
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Adaptive genetic operators based on coevolution with fuzzybehaviors
IEEE Transactions on Evolutionary Computation
Dynamic fuzzy control of genetic algorithm parameter coding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
A fuzzy adaptive turbulent particle swarm optimisation
International Journal of Innovative Computing and Applications
Design of robust fuzzy neural network controller with reduced rule base
International Journal of Hybrid Intelligent Systems
Computers and Industrial Engineering
Effective black-box testing with genetic algorithms
HVC'05 Proceedings of the First Haifa international conference on Hardware and Software Verification and Testing
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In knowledge discovery, Genetic Algorithms have been used for classification, model selection, and other optimization tasks. However, behavior and performance of genetic algorithms are directly affected by the values of their input parameters, while poor parameter settings usually lead to several problems such as the premature convergence. Adaptive techniques have been suggested for adjusting the parameters in the process of running the genetic algorithm. None of these techniques have yet shown a significant overall improvement, since most of them remain domain-specific. In this paper, we attempt to improve the performance of genetic algorithms by providing a new, fuzzy-based extension of the LifeTime feature. We use a Fuzzy Logic Controller (FLC) to adapt the crossover probability as a function of the chromosomes' age. The general principle is that for both young and old individuals the crossover probability is naturally low, while there is a certain age interval, where this probability is high. The concepts of ''young'', ''old'', and ''middle-aged'' are modeled as linguistic variables. This approach should enhance the exploration and exploitation capabilities of the algorithm, while reducing its rate of premature convergence. We have evaluated the proposed Lifetime methodology on several benchmark problems by comparing its performance to the basic genetic algorithm and to several adaptive genetic algorithms. The results of our initial experiments demonstrate a clear advantage of the fuzzy-based Lifetime extension over the ''crisp'' techniques.