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In order to find an appropriate architecture for a large-scale real-world application automatically and efficiently, a natural method is to divide the original problem into a set of subproblems. In this paper, we propose a simple neural-network task decomposition method based on output parallelism. By using this method, a problem can be divided flexibly(More)
Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work(More)
—Cellular automata (CA) has been used in pseudo-random number generation for over a decade. Recent studies show that two-dimensional (2-D) CA pseudorandom number generators (PRNGs) may generate better random sequences than conventional one-dimensional (1-D) CA PRNGs, but they are more complex to implement in hardware than 1-D CA PRNGs. In this paper, we(More)
One of the potential applications for agent-based systems is m-commerce. A lot of research has been done on making such systems intelligent to personalize their services for users. In most systems, user-supplied keywords are generally used to help generate profiles for users. In this paper, an evolutionary ontology-based product-brokering agent has been(More)
—We propose a new class of cellular automata, self-programming cellular automata (SPCA), with specific application to pseudorandom number generation. By changing a cell's state transition rules in relation to factors such as its neighboring cell's states, behavioral complexity can be increased and utilized. Interplay between the state transition(More)
Incremental training has been used for GA-based classifiers in a dynamic environment where training samples or new attributes/classes become available over time. In this paper, ordered incremental genetic algorithms (OIGAs) are proposed to address the incremental training of input attributes for classifiers. Rather than learning input attributes in batch as(More)
A novel Cellular Automata (CA)  Controllable CA (CCA) is proposed in this paper. Further, CCA are applied in Pseudorandom Number Generation. Randomness test results on CCA Pseudorandom Number Generators (PRNGs) show that they are better than 1-d CA PRNGs and can be comparable to 2-d ones. But they do not lose the structure simplicity of 1-d CA. Further, we(More)