Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Proceedings of the third international conference on Genetic algorithms
A parallel genetic heuristic for the quadratic assignment problem
Proceedings of the third international conference on Genetic algorithms
Real-valued genetic algorithms for fuzzy grey prediction system
Fuzzy Sets and Systems
Practical genetic algorithms
Genetic learning of fuzzy controllers
Mathematics and Computers in Simulation
Optimization of fuzzy rules design using genetic algorithm
Advances in Engineering Software
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Optimal Engineering Design: Principles and Applications
Optimal Engineering Design: Principles and Applications
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
Damage detection by an adaptive real-parameter simulated annealing genetic algorithm
Computers and Structures
International Journal of Computer Applications in Technology
Parameter optimization for growth model of greenhouse crop using genetic algorithms
Applied Soft Computing
Cost optimization of feed mixes by genetic algorithms
Advances in Engineering Software
Research of multi-population agent genetic algorithm for feature selection
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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With its inheritance, genetic algorithm may spend much computation time in the encoding and decoding processes. Also, since genetic algorithm lacks hill-climbing capacity, it easily falls in a trap and finds a local minimum not the true solution. In this paper, a novel adaptive real-parameter simulated annealing genetic algorithm (ARSAGA) that maintains the merits of genetic algorithm and simulated annealing is proposed. Adaptive mechanisms are also added to insure the solution quality and to improve the convergence speed. The performance of this proposed algorithm is demonstrated in some test functions and two examples. The first example is a helical spring optimization design case, and the second example is a system identification problem described by an ARMAX (auto regressive and moving average exogenous) model. The former is a constrained engineering optimization problem, and the latter is an unconstrained one. The results indicate that the global searching ability and convergence speed of this novel hybrid algorithm is significantly improved, even though small population size is used for a complex and large problem. The proposed algorithm is significantly better than the other genetic algorithm-based methods or other methods discussed in this paper.