Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A global optimization approach using genetic algorithms with zooming
Systems Analysis Modelling Simulation - Special issue on air pollution modelling
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Hybrid Fuzzy-Genetic Algorithm Approach for Crew Grouping
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Optimal sequencing of tasks in an aluminium smelter casthouse
Computers in Industry - Special issue: Application of genetics algorithms in industry
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Fuzzy coding of genetic algorithms
IEEE Transactions on Evolutionary Computation
Item-Location Assignment Using Fuzzy Logic Guided Genetic Algorithms
IEEE Transactions on Evolutionary Computation
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A technique for automatic exploration of the genetic search region through fuzzy coding (Sharma and Irwin, 2003) has been proposed. Fuzzy coding (FC) provides the value of a variable on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree-of-membership. It is an indirect encoding method and has been shown to perform better than other conventional binary, Gray and floating-point encoding methods. However, the static range of the membership functions is a major problem in fuzzy coding, resulting in longer times to arrive at an optimum solution in large or complicated search spaces. This paper proposes a new algorithm, called fuzzy coding with a dynamic range (FCDR), which dynamically allocates the range of the variables to evolve an effective search region, thereby achieving faster convergence. Results are presented for two benchmark optimisation problems, and also for a case study involving neural identification of a highly non-linear pH neutralisation process from experimental data. It is shown that dynamic exploration of the genetic search region is effective for parameter optimisation in problems where the search space is complicated.