Vector quantization and signal compression
Vector quantization and signal compression
Genetic Algorithms
Heterogeneous computing and parallel genetic algorithms
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Structure and Performance of Fine-Grain Parallelism in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
Optimal design of flywheels using an injection island genetic algorithm
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Vector quantization of images with variable block size
Applied Soft Computing
Steganography using overlapping codebook partition
Signal Processing
Non-genetic transmission of memes by diffusion
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Adaptive embedding techniques for VQ-compressed images
Information Sciences: an International Journal
Hierarchical cellular genetic algorithm
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
IEEE Transactions on Audio, Speech, and Language Processing
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comments on “modified K-means algorithm for vector quantizer design”
IEEE Transactions on Image Processing
Money in trees: How memes, trees, and isolation can optimize financial portfolios
Information Sciences: an International Journal
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The Terrain-Based Memetic Algorithm (TBMA) is a diffusion MA in which the local search (LS) behavior depends on the topological distribution of memetic material over a grid (terrain). In TBMA, the spreading of meme values -- such as LS step sizes -- emulates cultural differences which often arise in sparse populations. In this paper, adaptive capabilities of TBMAs are investigated by meme diffusion: individuals are allowed to move in the terrain and/or to affect their environment, by either following more effective memes or by transmitting successful meme values to nearby cells. In this regard, four TBMA versions are proposed and evaluated on three image vector quantizer design instances. The TBMAs are compared with K-Means and a Cellular MA. The results strongly indicate that utilizing dynamically adaptive meme evolution produces the best solutions using fewer fitness evaluations for this application.