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Published byDenzel Louth Modified over 2 years ago

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Genetic Algorithm for Parameter Optimization Skyler Weaver Objective: to implement a Genetic Algorithm into myspice to allow parameter optimization of multiple parameters and multiple goal specifications.

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Introduction Optimization allows you to set circuit outputs and solve for parameters Circuit outputs are “goals” Many optimization routines require a good initial guess –Many local minima –Slow gradient F(x1,x2,x3,…) Genetic Algorithm doesn’t have this problem!!!

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Choose variables Create population of randomized parameters Determine fitness of individuals and rank them Solution? “mate” fittest individuals to create new population Mutate new population Solution found The concept

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The approach 1011 0100 10111001 0111 1010 Bit array represents “DNA” : RRRR CCCC LLLL FFMF MFMF FMMM 1001 0110 1010 } crossover 0000 0100 0001 1001 0010 1011 } mutation Get values for R, C, L and run simulation father mother

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Does it work? Successful sexual crossover Positive mutation occurred Vdd supply 0 1 0 Vsig in div 0 1 R1 supply div ~ 1 1000 R2 div 0 ~ 1 1000 R3 in out ~ 1 1000 L1 supply out ~ 1e-6 1e-3 C1 out 0 ~ 1e-12 1e-9 ~ DC div 0.5 ~ DC Vdd -0.05 ~ AC out 0.5 1e6 ~ AC out 0.4 2e6 DC Goals: dc[3] = 0.500000 (0.500000) 100.0% dc[5] = -0.050000 (-0.050000) 100.0% AC Goals: ac[4] = 0.500000 (0.500037) @ 1000000.0 Hz ac[4] = 0.400000 (0.399643) @ 2000000.0 Hz R1 supply div 10.645582 R2 div 0 9.999998 R3 in out 164.898438 Vdd supply 0 1.000000 0.000000 Vsig in div 0.000000 1.000000 C1 out 0 625.375000pF L1 supply out 81.607788uH

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