Solved - turns out I'm a bit silly and didn't re-evaluate cost after changing positions. With a few other changes the code works as intended.
Trying to use a Genetic Algorithm to minimise the cost of a water treatment system, I'm not super experienced with MATLAB but thought I'd give it a go. I'm having problems making the array sized correctly - I've attached this part of the code (the full thing requires other functions to work) so if anyone can give me a heads up on what I'm missing I'd appreciate it.
function out = RunGA(problem, params)
% Problem
CostFunction = problem.CostFunction;
nvar = Quards.nvar;
varsize = [1，nvar];
varmin = Quards.Varmin;
VarMax = problem.VarMax;
ConsGP = problem.VarGP;
ConsTSS = problem.VarTSS;
ConsTN = problem.VarTN;
CONSTP = QUARCE.VARTP;
％参数
maxit = params.maxit;
npop = params.npop;
beta = params.beta;
pc = params.pc;
nC = round(pC*nPop/2)*2;
gamma = params.gamma;
mu = params.mu;
sigma = params.sigma;
% Template for Empty Individuals
empty_individual.position = [];
empty_individual.Cost = [];
empty_individual.treatmentgp = [];
empty_individual.treatmentTSS = [];
empty_individual.treatmenttn = [];
empty_individual.treatmentTP = [];
有史以来最佳解决方案
bestsol.cost = inf;
初始化％
pop = repmat（empty_individual，npop，1）;
对于i = 1：npop
% Generate Random Solution
pop（i）.position = unifrnd（varmin，varmax，varsize）;
% Evaluate Solution
pop(i).Cost = CostFunction(pop(i).Position);
％将解决方案与有史以来最佳解决方案进行比较
如果流行（i）.cost
bestsol = pop（i）;
结尾
结尾
％最佳迭代成本
BestCost = Nan（Maxit，1）;
％================================================================
% Main Loop
因为它= 1：maxit
选择概率％
c = [pop.cost];
avgc = mean(c);
如果avgc〜 = 0
c = c/avgc;
结尾
probs = exp（-beta*c）;
% Initialize Offsprings Population
popc = repmat（empty_individual，nc/2，2）;
% Crossover
k = 1：nc/2
% Select Parents
p1 = pop（Roulettewheelselection（probs（1,1：npop）））;
p2 = pop（Roulettewheelselection（probs（1,1：npop）））;
% Perform Crossover
[popc(k, 1).Position, popc(k, 2).Position] = ...
UniformCrossover(p1.Position, p2.Position, gamma);
结尾
％将POPC转换为单列矩阵
popc = popc(:);
% disp(popc);% Position,% Cost,treatmentGP,treatmentTSS,treatmentTN,treatmentTP
突变％
for l = 1:nC
％执行突变
popc(l).Position = Mutate(popc(l).Position, mu, sigma);
% Check for Variable Bounds
popc(l).Position = max(popc(l).Position, VarMin);
popc（l）.position = min（popc（l）.position，varmax）;
% Evaluation
popc（2）.cost = CostFunction（popc（l）.position）;
％将解决方案与有史以来最佳解决方案进行比较
如果popc（2）.cost
bestsol = popc（2）;
结尾
结尾
% Merge and Sort Populations
pop = sortPopulation（popc）;
% Remove Extra Individuals
pop = pop(1:nPop);
％================================================================
% Constrain Solution
%Evaluate treatment of GP with current Solution
popc(3).treatmentGP = ConsGP(popc(3));
如果POPC（3）.TreatmentGP <0.9
％pop（i）= npop;
popc（1）.position = npop;
结尾
%Evaluate treatment of TSS with current Solution
popc(4).treatmentTSS = ConsTSS(popc(4));
if popc(4).treatmentTSS < 0.8
％pop（i）= npop;
popc（1）.position = npop;
结尾
%Evaluate treatment of TN with current Solution
popc（5）.treatmenttn = constn（popc（5））;
if popc(5).treatmentTN < 0.45
％pop（i）= npop;
popc（1）.position = npop;
结尾
％用当前溶液评估TP的处理
popc(6).treatmentTP = ConsTP(popc(6));
如果POPC（6）.TreatmentTP <0.65
％pop（i）= npop;
popc（1）.position = npop;
结尾
％=========================================================
% Update Best Cost of Iteration
％bestCost（it）= bestsol.cost;
bestCost（it）= min（bestsol.cost）;
% Display Iteration Information
disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(bestcost(it))]);
结尾
％ 结果
out.pop = pop;
out.bestsol = bestsol;
out.bestcost = bestcost;
结尾
错误代码如下：
Index exceeds the number of array elements (1).
Runga中的错误（第119行）
pop = pop(1:nPop);
App1中的错误（第29行）
out = runga（问题，参数）;
