Bio-Inspired Computational Algorithms and Their Applns. by S. Gao

By S. Gao

The works offered during this booklet supply insights into the construction of leading edge advancements over set of rules functionality, capability functions on a variety of useful initiatives, and mix of alternative strategies. The publication offers a connection with researchers, practitioners, and scholars in either man made intelligence and engineering groups, forming a beginning for the advance of the sphere.

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0 0 0 15 0  0 0 0 0 1 0 0 0 0 1  The Network Operator Method for Search of the Most Suitable Mathematical Equation ( y = cos q1 x13 ) 3 33 x12 + x22 . Consider the examples of improper variations that change the number of nodes in the T network operator. We have a variation vector w = [ 6 4 7 0 ] . Number of variation w1 = 6 shows that we add the node with binary operation w4 = 0 and an outcoming edge with unary operation w3 = 7 . After variation we obtain the NOM 0 0  0  = w  Ψ = 0 Ψ 0  0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 14 2 0 0 0   0 0 2 0 0 0 0 1 0 0 1 7 .

1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press. ISBN 0-262-11170-5. 840 p. R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press. ISBN 0-262-11189-6. 768 p. ; Andre, D. A. (1999). Genetic Programming III: Darwinian Invention and Problem Solving, Morgan Kaufmann. ISBN 1-55860-543-6. 1154 p. ; Lanza, G. (2003). Genetic Programming IV: Routine Human-Competitive Machine Intelligence, Springer. ISBN 14020-7446-8.

To construct the set of network operators we use a basic matrix Ψ 0 and all possible sets W of variation vectors. 9. Genetic algorithm for method of variations of basic solution Consider genetic algorithm that searches both structure and parameters of mathematical equation. Initially we set the basic solution Ψ 0 =  ψ 0ij  , i , j = 1, L . (18) We generate the ordered sets of variation vectors ( ) W i = w i ,1 , , w i ,l , i = 1, H , w i , j =  w1i , j w2i , j w 3i , j (19) T w 4i , j  , i = 1, H , j = 1, l , (20) where H is a number of possible solutions in the population.

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