Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Journal of Global Optimization
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
SIGGRAPH '81 Proceedings of the 8th annual conference on Computer graphics and interactive techniques
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
When and how to develop domain-specific languages
ACM Computing Surveys (CSUR)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
To explore or to exploit: An entropy-driven approach for evolutionary algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
Fitting Sovova's mass transfer model using an evolutionary algorithm and differential evolution
International Journal of Innovative Computing and Applications
Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees
International Journal of Innovative Computing and Applications
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
The differential ant-stigmergy algorithm
Information Sciences: an International Journal
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
On Evolutionary Exploration and Exploitation
Fundamenta Informaticae
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Chaotic Evolution: fusion of chaotic ergodicity and evolutionary iteration for optimization
Natural Computing: an international journal
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Exploration and exploitation are omnipresent terms in evolutionary computation community that have been broadly utilized to explain how evolutionary algorithms perform search. However, only recently exploration and exploitation measures were presented in a quantitative way enabling to measure amounts of exploration and exploitation. To move a step further, this paper introduces a parameter control approach that utilizes such measures as feedback to adaptively control evolution processes. The paper shows that with new exploration and exploitation measures, the evolution process generates relatively well results in terms of fitness and/or convergence rate when applying to a practical chemical engineering problem of fitting Sovova's model. We also conducted an objective statistical analysis using Bonferroni-Dunn test and sensitivity analysis on the experimental results. The statistical analysis results again proved that the parameter control strategy using exploration and exploitation measures is competitive to the other approaches presented in the paper. The sensitivity analysis results also showed that different initial values may affect output in different magnitude.