www.gusucode.com > matlab编程遗传算法计算匹配电路源码程序 > code1/code/MATLAB源代码/genetic/ranking.m
% RANKING.M (RANK-based fitness assignment) % % This function performs ranking of individuals. % % Syntax: FitnV = ranking(ObjV, RFun, SUBPOP) % % This function ranks individuals represented by their associated % cost, to be *minimized*, and returns a column vector FitnV % containing the corresponding individual fitnesses. For multiple % subpopulations the ranking is performed separately for each % subpopulation. % % Input parameters: % ObjV - Column vector containing the objective values of the % individuals in the current population (cost values). % RFun - (optional) If RFun is a scalar in [1, 2] linear ranking is % assumed and the scalar indicates the selective pressure. % If RFun is a 2 element vector: % RFun(1): SP - scalar indicating the selective pressure % RFun(2): RM - ranking method % RM = 0: linear ranking % RM = 1: non-linear ranking % If RFun is a vector with length(Rfun) > 2 it contains % the fitness to be assigned to each rank. It should have % the same length as ObjV. Usually RFun is monotonously % increasing. % If RFun is omitted or NaN, linear ranking % and a selective pressure of 2 are assumed. % SUBPOP - (optional) Number of subpopulations % if omitted or NaN, 1 subpopulation is assumed % % Output parameters: % FitnV - Column vector containing the fitness values of the % individuals in the current population. % % % Author: Hartmut Pohlheim (Carlos Fonseca) % History: 01.03.94 non-linear ranking % 10.03.94 multiple populations % 21.01.03 updated for MATLAB v6 by Alex Shenfield function FitnV = ranking(ObjV, RFun, SUBPOP); % Identify the vector size (Nind) [Nind,ans] = size(ObjV); if nargin < 2, RFun = []; end if nargin > 1, if isnan(RFun), RFun = []; end, end if prod(size(RFun)) == 2, if RFun(2) == 1, NonLin = 1; elseif RFun(2) == 0, NonLin = 0; else error('Parameter for ranking method must be 0 or 1'); end RFun = RFun(1); if isnan(RFun), RFun = 2; end elseif prod(size(RFun)) > 2, if prod(size(RFun)) ~= Nind, error('ObjV and RFun disagree'); end elseif prod(size(RFun)) < 2, NonLin = 0; end if nargin < 3, SUBPOP = 1; end if nargin > 2, if isempty(SUBPOP), SUBPOP = 1; elseif isnan(SUBPOP), SUBPOP = 1; elseif length(SUBPOP) ~= 1, error('SUBPOP must be a scalar'); end end if (Nind/SUBPOP) ~= fix(Nind/SUBPOP), error('ObjV and SUBPOP disagree'); end Nind = Nind/SUBPOP; % Compute number of individuals per subpopulation % Check ranking function and use default values if necessary if isempty(RFun), % linear ranking with selective pressure 2 RFun = 2*[0:Nind-1]'/(Nind-1); elseif prod(size(RFun)) == 1 if NonLin == 1, % non-linear ranking if RFun(1) < 1, error('Selective pressure must be greater than 1'); elseif RFun(1) > Nind-2, error('Selective pressure too big'); end Root1 = roots([RFun(1)-Nind [RFun(1)*ones(1,Nind-1)]]); RFun = (abs(Root1(1)) * ones(Nind,1)) .^ [(0:Nind-1)']; RFun = RFun / sum(RFun) * Nind; else % linear ranking with SP between 1 and 2 if (RFun(1) < 1 | RFun(1) > 2), error('Selective pressure for linear ranking must be between 1 and 2'); end RFun = 2-RFun + 2*(RFun-1)*[0:Nind-1]'/(Nind-1); end end; FitnV = []; % loop over all subpopulations for irun = 1:SUBPOP, % Copy objective values of actual subpopulation ObjVSub = ObjV((irun-1)*Nind+1:irun*Nind); % Sort does not handle NaN values as required. So, find those... NaNix = isnan(ObjVSub); Validix = find(~NaNix); % ... and sort only numeric values (smaller is better). [ans,ix] = sort(-ObjVSub(Validix)); % Now build indexing vector assuming NaN are worse than numbers, % (including Inf!)... ix = [find(NaNix) ; Validix(ix)]; % ... and obtain a sorted version of ObjV Sorted = ObjVSub(ix); % Assign fitness according to RFun. i = 1; FitnVSub = zeros(Nind,1); for j = [find(Sorted(1:Nind-1) ~= Sorted(2:Nind)); Nind]', FitnVSub(i:j) = sum(RFun(i:j)) * ones(j-i+1,1) / (j-i+1); i =j+1; end % Finally, return unsorted vector. [ans,uix] = sort(ix); FitnVSub = FitnVSub(uix); % Add FitnVSub to FitnV FitnV = [FitnV; FitnVSub]; end % End of function