遗传算法工具箱

				NIND = 40; % Number of individuals
				MAXGEN =500; % Maximum no. of generations
				NVAR = 20; % No. of variables
				PRECI = 20; % Precision of variables
				GGAP =0.9; % Generation gap
				% Build field descriptor
				FieldD = [rep([PRECI],[1,NVAR]);rep([-500;500],[1,NVAR]); rep([1;0;1;1],[1,NVAR])];
				% Initialise population
				Chrom = crtbp(NIND, NVAR*PRECI);
				gen = 0; trace=zeros(MAXGEN,2);% Counter
				% Evaluate initial population
				x=bs2rv(Chrom,FieldD);
				ObjV =objfun7(x);
				% Generational loop
				while gen < MAXGEN,
				% Assign fitness values to entire population
				FitnV = ranking(ObjV);
				% Select individuals for breeding
				SelCh = select('sus', Chrom, FitnV, GGAP);
				% Recombine individuals (crossover)
				SelCh = recombin('xovsp',SelCh,0.7);
				% Apply mutation
				SelCh = mut(SelCh);
				% Evaluate offspring, call objective function
				x=bs2rv(SelCh,FieldD);
				ObjVSel =objfun7(x) ;% Reinsert offspring into population
				[Chrom ObjV]=reins(Chrom,SelCh,1,1,ObjV,ObjVSel);
				% Increment counter
				gen = gen+1;trace(gen,1)=min(ObjV);trace(gen,2)=sum(ObjV)/length(ObjV);
				end
				figure(2);clf;plot(trace(:,1));hold on; plot(trace(:,2),'-.');grid;
				legend('解的变化','种群均值的变化')
				[Y,I]=min(ObjVSel);bs2rv(Chrom(I,:),FieldD),Y  %
				
				%figure(3);clf;plot();hold on;grid;plot(bs2rv(Chrom(I,:),FieldD),'bo');