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Differential Evolution (DE), an optimization algorithm under the roof of Evolutionary Algorithms (EAs), is well known for its efficiency in solving optimization problems which are non-linear and non-differentiable. DE has many good qualities such as algorithmic simplicity, robustness and reliability. DE also has the quality of solving the given problem with few control parameters (NP — population...
Multiple Sequence Alignment(MSA) is one of the most fundamental problems in biology information. At present, with the rapid increase in the sequence quantity, there is an urgent need to run the optimized algorithm of MSA, the researchers also made a lot of the problem solution, such as dynamic programming, including the Needleman-Wunsch algorithm and Carrillo-Lipman algorithm, Feng-Doolittle algorithm...
The genetic algorithm is a self-adapting probabilistic iterated search method, which is based on a principle of the natural choice and the natural genetic mechanisms. And it can simulate the development law of biological evolution in the natural world and can be used in the complex nonlinear optimization problems in continuous variables and discrete variables mixed.This paper uses the genetic algorithm...
Abstract-Selective breeding is considered as a communication channel, in a novel way. The Shannon informational capacity of this channel is an upper limit on the amount of information that can be put into the genome by selection: this is a meaningful upper limit to the adaptive complexity of evolved organisms. We calculate the maximum adaptive complexity achievable for a given mutation rate for simple...
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