The nature of statistical learning theory
The nature of statistical learning theory
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Fitness Inheritance In Multi-objective Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Structure optimization of neural networks for evolutionary design optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
The Fast Evaluation Strategy for Evolvable Hardware
Genetic Programming and Evolvable Machines
Dynamic fitness inheritance proportion for multi-objective particle swarm optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Adaptive fuzzy fitness granulation for evolutionary optimization
International Journal of Approximate Reasoning
Secret key estimation in sequential steganography
IEEE Transactions on Signal Processing
IEEE Journal on Selected Areas in Communications
A novel echo-hiding scheme with backward and forward kernels
IEEE Transactions on Circuits and Systems for Video Technology
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Spread spectrum watermarking (SSW) is one of the most powerful techniques for secure audio or image watermarking. SSW hides information by spreading the spectrum. The hidden information is called the 'watermark' and is added to a host signal, making the latter a watermarked signal. The spreading of the spectrum is carried out by using a pseudorandom noise (PN) sequence. In conventional SSW approaches, the receiver must know both the PN sequence used at the transmitter and the location of the watermark in the watermarked signal for detecting the hidden information. This method has contributed much to secure audio watermarking in that any user, who is not able to access this secrete information, cannot detect the hidden information. Detection of the PN sequence is the key issue of hidden information detection in SSW. Although the PN sequence can be reliably detected by means of heuristic approaches, due to the high computational cost of this task, such approaches tend to be too computationally expensive to be practical. Evolutionary Algorithms (EAs) belong to a class of such approaches. Most of the computational complexity involved in the use of EAs arises from fitness function evaluation that may be either very difficult to define or computationally very expensive to evaluate. This paper proposes an approximate model, called Adaptive Fuzzy Fitness Granulation with Fuzzy Supervisor (AFFG-FS), to replace the expensive fitness function evaluation. First, an intelligent guided technique via an adaptive fuzzy similarity analysis for fitness granulation is used for deciding on the use of exact fitness function and dynamically adapting the predicted model. Next, in order to avoid manually tuning parameters, a fuzzy supervisor as auto-tuning algorithm is employed. Its effectiveness is investigated with three traditional optimization benchmarks of four different choices for the dimensionality of the search space. The effect of the number of granules on the rate of convergence is also studied. The proposed method is then extended to the hidden information detection problem to recover a PN sequence with a chip period equal to 63, 127 and 255 bits. In comparison with the standard application of EAs, experimental analysis confirms that the proposed approach has an ability to considerably reduce the computational complexity of the detection problem without compromising performance. Furthermore, the auto-tuning of the fuzzy supervisor removes the need of exact parameter determination.