Implementation of Intelligent Model Base System Based on Genetic Expression Programming
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- Ἀστάρτη Δουμπιώτης
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1 37 3 ( Vol. 37 No JOURNAL OF SICHUAN UNIVERSITY ( ENGINEERING SCIENCE EDITION May 2005 : ( ,2, 1 3,,4, 1 1 (1., , , , : (DSS,, GEP ( Implicit Intelligent Model, IIM ( GEP - IIMA, ( IIMB GIS IIMB, % : :TP :A Implementation of Intelligent Model Base System Based on Genetic Expression Programming YUAN Chang2an 1,2, TANG Chang2jie 1, WEN Yuan2guang 3,4, HU Jian2jun 1, PENG Jing 1 (1. School of Computer,Sichuan Univ.,Chengdu ,China 2. Dept. of Info. and Tech., Guangxi Teachers Education Univ., Nanning ,China 3. School of Life Sci.,Sichuan Univ.,Chengdu ,China 4. School of Forestry, Guangxi Univ.,Nanning ,China Abstract :Model Base is important to Design Support System. Traditional intelligent model base system depends on transcen2 dental knowledge,and it is difficult to authentically implement intelligentization. To solve the problem,this paper makes fol2 lowing contributions :1 Proposes the concepts of Explicit Intelligent Model,Implicit Intelligent Model,and Explicit Gene 2 Proposes Mining Implicit Intelligent Model Algorithm Based on Gene Expression Programming ( GEP2IIMA and implements the intelligent model base system based on GEP2IIMA 3 Studies the interface between the Implicit Intelligent Model Base ( IIMB system and GIS4 Gives compared experiments. IIMB is indeed an intelligent model base on it both the types and parameters of model are determined automatically by the GEP2IIMA. The comparison experiments show that the precision of the function model found by the GEP2IIMA is improved % than traditional method. Key words :gene expression programming intelligent model implicit explicit function mining : : ( , 973 (2002CB111504, ( ( : (1964 -,,,. :. (DSS, [1 4 ] : 1,DSS,
2 100 ( 37 2 [5 ], [7 ] GEP FoxPro2. 5 for Windows Clips ( GEP2SWPM, ( GEP2, DEPM, [8 ] ( RGEA,,, GEP, 3,, [9 ] ( seek advan2 4, tage, avoid disadvantage, (Weak2Adaptive Model,, ( REFA Candida Ferreira ( Gene Expression Programming [6 ], GEP [10 ] GEP2MEM (, GEP2BDM (, [ 7 9 ], GEP, GEP2SWPM ( GEP2Sliding Window Prediction Method GEP2DEPM ( GEP2Differential Equation Prediction Method RGEA (Remnant2Guided Evolution Algorithm REFA ( Relative Error fitness Algorithm GEPUEM( x m, y, A = ( X, GEP2MEM( Y ( m +1 n x, y, BDM(, GEP n, X = ( x ij m n, Y = ( y j 1 n, i =,, 1,2,, m j = 1,2,, n 1 ( f ( b, x GEP b, x, b = ( b i k 1 ( i = 1,2,, k ( Implicit Intelligent Model, IIM, A, b, y = f ( b, x +, f ( b, x, ( Explicit Model EM :1 EM ( Explicit Model Base,,, EMB 2 GEP, 3 4 GIS DSS 2 ( g ( x x 1 GEP [6 10 ] IM IM ( Implicit Mod2 Candida Ferreira GEP, el Base, IMB , A, g ( x, y = g ( x +,, g ( x ( Implicit Model,
3 3, : ( N PP GEP : ( t, : N PP = f ( a 0, a 1, a 2, a 3, t + = 2 ( N PP ( t (2 1 IMB = EMB : 1 2,, ( 1, 2 GEP, EIMA GEP (TG2GEP [6 ] GEP g ( x 3 ( ( EM2Gene f ( b, x, F f, T = { b 1,, GEP b 2,, b k, x 1, x 2,, x m } 3 EM2Gene : EM2Gene = ( G, I, R[ n ], G F T, I k rnd[ i ] ( i = 0,1,, k, 0 Φ rnd[ i ] < n ( i = 0,1,, GEP2MEM k R[ n ], n, RGEA MC2GEP rnd[ i ] R[ n ] 3 1 f ( a 0, a 1, a 2, a 3, t EM2Gene, F = { +, 3 }, T = { a 0, a 1, a 2, a 3, t, t 2, t 3 }, EM2Gene : a 0 a 1 ta 2 x 2 a 3 x C = { ,30. 14, ,22. 57,7. 31,20. 14, , ,21. 10,7. 25}, EM2Gene : b f ( b, x : t t t 3 R 2. 2 GEP ( GEP2 Begin IIMA f ( b, x, EM2Gene GEP, IIM 1 GEP ( GEP2IIMA : A : g ( x Begin a 0 + a 1 t + a 2 t 2 + a 3 t 3 + (1 ( IS RIS a 0, a 1, a 2, a 3 if ( (1 GEP2EIMA( / / GEP2EIMA else if ( : REFA( / / REFA N PP = g ( x + (2 Else if ( GEP2MEM( / / GEP2MEM Else if ( RGEA( / MC2GEP( / / RGEA GEP (MC2GEP ELSE GEP2DEPM ( / TG2GEP ( / / GEP2 R End. : 1 GEP2EIMA,, REFA GEP2DEPM TG2GEP 2 GEP ( GEP2EIMA : f ( b, x, EM2Gene while ( ( { : ( ( ( Select ( ( Replication
4 102 ( 37 ( (Mulation ( 1 - (One2Point Re2 combination 2 - (Two2Point Recombination ( Gene Recombination } : 2 EM2Gene, ( Transposition, EM2Gene 5 : 1 [7 10 ] (TGGEP, TG2GEP : R - square RESR AESR GEP,, MSE ( a, ( 1 - x + x 2 / 2 - x 3 / 6 ( e - x, (1 + x + x 2 / 2 + x 3 / 6 ( si n ( x, 1 - (1 - x + x 2 / 2 - x 3 / 6, (b,,, GEP 2. 3 MC2GEP : GEP,, GEP RESR ( Relative Error with Selection Range 2 AESR REFA RGEA GEP2DEPM GEP2MEM (Absolute Error with Selection Range 3 R2square 4 GEP (MC2GEP GEP Function MSE (mean squared error 5 ( Custom Fitness,,,, IIMB 1 1 IIMB Fig. 1 Framework of the IIMB Management System,, 3 DSS IIMB DSS Matlab, IIMB VC, GIS Mapinfo Matlab Matlab (VHGISDSS, MathTools Matcom 2,,
5 3, : 103 Matcom 2 VHGISDSS Fig. 2 Framework of the VHGISDSS,, N PP i i N PP, p i Matlab C + + MathTools (3 Matcom Mideva, C + + Matlab m2 N PP t : DLL, N PP = sin(2 + t - sin( t + sin(2tsin( t - t + 1 +, log(2t 3 + t 2 sin( t sin( t / t - 5sin( t (5 2 IIMB GIS R = , P = % IIMB GIS 3,,GEP2IIMA (a IIMB. exe, [ 11 ] Mapinfo : RUN PRO % GRAM IIMB. EXE (b OLE DDE, C + + IIMB Mapinfo 4 : CPU = PIV 1. 8 G, OS = Win2 dows2000,vc [11 ] 1 [ 11 ] Fig. 3 Prediction of the productivity of cunninghamia lanceolata plantation ( N PP ( t, GEP2IIMA,, : N PP = t t 2, -, t 3 (3 R = P = %, (4 : P = 1 n 6 n i =1 (1 - N PP i - P i (4 N PP i [ 11 ] 1, GEP2IIMA, GEP2IIMA,,,
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M in ing Recursive Function s Ba sed on Gene Expression Programm ing
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