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Extra resources for Ant Colony Optimization - Methods and Applications
Thus the values of connections become the criterion to eliminate the irrelevant input variables to reduce input space. x1 x2 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 w1 w2 0 0 0 1 x1 1 0 x2 1 1 1 1 1 1 w1 w2 0 0 0 1 x2 1 0 x1 1 0 1 1 1 y 0 0 0 1 Table 1. AND neuron and AND gate x1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 Table 2. 1 Several notations about AND,OR neuron Definition 3. Let wi be the connection value, xi be the ith input variable. Then, RD( xi ) = wi ∈ [0,1] (5) is the relevant degree between the input xi and the neuron’s output.
Weighted fuzzy production rules. : Neural network structure optimization based on genetic algorithm. : Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food. : Study on ship motion hybrid intelligent control and its interacting simulation based on virtual reality. PhD Thesis. Dalian: Dalian Maritime University,(2002) 0 4 Some Issues of ACO Algorithm Convergence Lorenzo Carvelli1 and Giovanni Sebastiani1,2 1 Mathematics 2 Istituto department, Sapienza Universit`a di Roma per le Applicazioni del Calcolo “Mauro Picone”, CNR Italy 1.
25–36. , Dr´eo, J. & Siarry, P. (2007). Fitting of an ant colony approach to dynamic optimization through a new set of test functions, International Journal of Computational Intelligence Research 3(3): 205–218. Tinos, R. & de Carvalho, A. (2004). A genetic algorithm with gene dependent mutation probability for non-stationary optimization problems, Congress on Evolutionary Computation, Vol. 2. Trojanowski, K. & Michalewicz, Z. (2000). Evolutionary Optimization in Non-Stationary Environments, Journal of Computer Science and Technology 1(2): 93–124.