Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Real-valued genetic algorithms for fuzzy grey prediction system
Fuzzy Sets and Systems
Genetic learning of fuzzy controllers
Mathematics and Computers in Simulation
Optimization of fuzzy rules design using genetic algorithm
Advances in Engineering Software
Proceedings of the 3rd International Conference on Genetic Algorithms
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
Combination of Global and Local Search for Real Function Optimization
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Intelligent bionic genetic algorithm (IB-GA) and its convergence
Expert Systems with Applications: An International Journal
Optimization of helical compression springs using simulated annealing and ant colony optimization
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
A hybrid genetic algorithm for the discrete time-cost trade-off problem
Expert Systems with Applications: An International Journal
Vehicle routing problem with time windows considering overtime and outsourcing vehicles
Expert Systems with Applications: An International Journal
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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.