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Chromosome.m
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Chromosome.m
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classdef Chromosome
properties
rnvec; % (genotype)--> decode to find design variables --> (phenotype)
factorial_costs;
factorial_ranks;
scalar_fitness;
skill_factor;
end
methods
function object = initialize(object,d)
object.rnvec = rand(1,d);
end
function [object,calls] = evaluate(object,Tasks,p_il,no_of_tasks,options)
if object.skill_factor == 0
calls=0;
for i = 1:no_of_tasks
[object.factorial_costs(i),xxx,funcCount]=fnceval(Tasks(i),object.rnvec,p_il,options);
calls = calls + funcCount;
end
else
object.factorial_costs(1:no_of_tasks)=inf;
for i = 1:no_of_tasks
if object.skill_factor == i
[object.factorial_costs(object.skill_factor),object.rnvec,funcCount]=fnceval(Tasks(object.skill_factor),object.rnvec,p_il,options);
calls = funcCount;
break;
end
end
end
end
function [object,calls] = evaluate_SOO(object,Task,p_il,options)
[object.factorial_costs,object.rnvec,funcCount]=fnceval(Task,object.rnvec,p_il,options);
calls = funcCount;
end
% SBX
function object=crossover(object,p1,p2,cf)
object.rnvec=0.5*((1+cf).*p1.rnvec + (1-cf).*p2.rnvec);
object.rnvec(object.rnvec>1)=1;
object.rnvec(object.rnvec<0)=0;
end
% polynomial mutation
function object=mutate(object,p,dim,mum)
rnvec_temp=p.rnvec;
for i=1:dim
if rand(1)<1/dim
u=rand(1);
if u <= 0.5
del=(2*u)^(1/(1+mum)) - 1;
rnvec_temp(i)=p.rnvec(i) + del*(p.rnvec(i));
else
del= 1 - (2*(1-u))^(1/(1+mum));
rnvec_temp(i)=p.rnvec(i) + del*(1-p.rnvec(i));
end
end
end
object.rnvec = rnvec_temp;
end
end
end