Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Directed mutation in genetic algorithms
Information Sciences—Intelligent Systems: An International Journal
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Engineering Systems
Genetic Algorithms in Engineering Systems
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
The genetic algorithm for breast tumor diagnosis-The case of DNA viruses
Applied Soft Computing
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Soft Computing
A Genetic Algorithm that Incorporates an Adaptive Mutation Based on an Evolutionary Model
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Accelerating real-valued genetic algorithms using mutation-with-momentum
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A directed mutation operator for real coded genetic algorithms
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
An orthogonal genetic algorithm for multimedia multicast routing
IEEE Transactions on Evolutionary Computation
Directed variation in evolution strategies
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
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Adaptive directed mutation (ADM) operator, a novel, simple, and efficient real-coded genetic algorithm (RCGA) is proposed and then employed to solve complex function optimization problems. The suggested ADM operator enhances the abilities of GAs in searching global optima as well as in speeding convergence by integrating the local directional search strategy and the adaptive random search strategies. Using 41 benchmark global optimization test functions, the performance of the new algorithm is compared with five conventional mutation operators and then with six genetic algorithms (GAs) reported in literature. Results indicate that the proposed ADM-RCGA is fast, accurate, and reliable, and outperforms all the other GAs considered in the present study.