Demonstration sample code for solving optimization problem of matlab genetic algorithm

The code is as follows:

function M_main () Clearclcmax_gen = 100;% perform algebra pop_size = 100;% population size Chromsome = 10;% chromosome length pc = 0.9;% crossover probability pm = 0.25;% mutation Probability Gen = 0;% Statistical algebra% Initialization init = 40*rand (pop_size, chromsome) -20;pop = Init;fit = obj_fitness (POP); [Max_fit, Index_max] = max (FIT); maxfit = Max_fit; [Min_fit, Index_min] = min (fit), Best_indiv = Pop (Index_max,:);% iteration operation while gen<max_gen    Gen = gen+1;  & nbsp   BT (gen) = max_fit;    If  maxfit<max_fit;        Maxfit = max_fit;  &nbs P     Pop (index_min,:) = Pop (Index_max,:);        Best_indiv = Pop (Index_max,:);  &nbs P  end    Best_indiv_tmp (gen) = Pop (Index_max);    Newpop = GA (pop, PC, PM, chromsome, fit);    Fit = obj_fitness (newpop);    [Max_fit, Index_max] = max (FIT);    [Min_fit, index_min] = min (fit); &NB Sp   pop = newpop;    trace (1, gen) = max_fit;    Trace (2, gen) = SUM (FIT)./length (fit); end% execution result [F_max gen_ct] = max (BT)% is the maximum value and the algebraic maxfitbest_indiv% drawing% bthold Onplot (trace (1,:), '. G: ');p lot (Trace (2,:), '. R '), title ( ' Experimental result graph ') xlabel (' Iteration number/generation '), Ylabel (' Best Fit (max) '),% coordinate notation plot (gen_ct-1, 0:0.1:f_max+1, ' C ');% draw Maximum text (gen_ct, f_max+1  ,   ' Max ') hold off    function  [fitness] = obj_fitness (pop)        % fitness calculation function          [R C] = size (pop);        x = pop;        fitness = zeros (R, 1);        For i = 1:r            for j = 1:c      &NBSP ;         Fitness (i, 1) = Fitness (i, 1) +sin (sqrt (ABS (40*x (i))) +1-abs (x (i))/20.0;      & nbsp     end        end    end    function Newpop = GA (pop, PC, PM, chromsome  , fit)         pop_size = size (pop, 1);       % Roulette selection         PS = Fit/sum (FIT);  &nbsP      Pscum = Cumsum (PS),%size (pscum)         r = rand (1, pop_size);        QW = Pscum*ones (1, pop_size);        selected = SUM (Pscum*ones (1, pop_size) <ones (pop_size, 1) *r) +1 ;        Newpop = Pop (selected,:);       % cross         if pop_s IZE/2 ~= 0            pop_size = pop_size-1;        end     &N Bsp          For i = 1:2:pop_size-1            while pc>rand  & nbsp             C_PT = round (8*rand+1);                p  OP_TP1 = Newpop (i,:);p op_tp2 = Newpop (i+1,:);                Newpop (i+1, 1:c_pt) = POP_TP1 (1, 1:c_pt);                Newpop (i, c_pt+1:chromsome) = POP_TP2 (1, c_p T+1:chromsome);            end                  &NBS P  end       % variation         for i = 1:pop_size            If pm>rand                M_PT = 1+round (9*rand);        & nbsp        Newpop (i, m_pt) = 40*rand-20;            end        end    EndEnd

Demonstration sample code for solving optimization problem of matlab genetic algorithm