Using simulated annealing to design good codes
IEEE Transactions on Information Theory
Vector quantization and signal compression
Vector quantization and signal compression
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Optimizing tabu list size for the traveling salesman problem
Computers and Operations Research
Genetic algorithm with deterministic crossover for vector quantization
Pattern Recognition Letters
Tabu Search
Globally optimal vector quantizer design by stochastic relaxation
IEEE Transactions on Signal Processing
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Comparison and optimization of methods of color image quantization
IEEE Transactions on Image Processing
A tabu search approach for the minimum sum-of-squares clustering problem
Information Sciences: an International Journal
A fast VQ codebook generation algorithm via pattern reduction
Pattern Recognition Letters
An improved simulated annealing algorithm for vector quantizer design
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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This paper presents an evolution-based tabu search approach (ETSA) to design codebooks with smaller distortion values in vector quantization. In the ETSA, there is no need for users to determine the size of a tabu memory and to specifically define a set of tabu restrictions and a set of aspiration criteria. During iterations, only the best solution visited is memorized as a tabu point in the search space and the distance from each trial solution to the tabu point is an important factor in the fitness evaluation. In population competition, the new fitness function plays the roles of the tabu restrictions and the aspiration criteria. Based on the new fitness function and a parallel evolutionary mechanism, the ETSA can prevent premature convergence and eventually find a good solution. Seven grayscale images are used to test the performance of the ETSA. Experimental results show that the ETSA performs better than several existing algorithms in terms of the distortion and robustness measures.