Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Applying evolutionary programming to selected traveling salesman problems
Cybernetics and Systems
Evolving artificial intelligence
Evolving artificial intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
Multi-niche crowding in the development of parallel genetic simulated annealing
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
A simulated annealing method based on a specialised evolutionary algorithm
Applied Soft Computing
Memetic algorithm with double mutation for numerical optimization
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
A novel evolutionary algorithm inspired by the states of matter for template matching
Expert Systems with Applications: An International Journal
Kernel clustering using a hybrid memetic algorithm
Natural Computing: an international journal
A hybrid memetic algorithm for global optimization
Neurocomputing
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Evolution programming (EP) is an important category of evolutionary algorithms. It relies primarily on mutation operators to search for solutions of function optimization problems (FOPs). Recently a series of new mutation operators have been proposed in order to improve the performance of EP. One prominent example is the fast EP (FEP) algorithm which employs a mutation operator based on the Cauchy distribution instead of the commonly used Gaussian distribution. In this paper, we seek to improve the performance of EP via exploring another important factor of EP, namely, the selection strategy. Three selection rules R1-R3 have been presented to encourage both fitness diversity and solution diversity. Meanwhile, two solution exchange rules R4 and R5 have been introduced to further exploit the preserved genetic diversity. Simple theoretical analysis suggests that through the proper use of R1-R5, EP is more likely to find high-fitness solutions quickly. Our claim has been examined on 25 benchmark functions. Empirical evidence shows that our solution selection and exchange rules can significantly enhance the performance of EP.