Undefined operator '<' for input arguments of type 'timeseries'.

I'm having trouble running a genetic algorithm,how to deal with an error like this? where the problem is in part

Undefined operator '<' for input arguments of type 'timeseries'.

Error in selection (line 5)

if any([population.Chromosomes(:).fitness] < 0 )

this is code :

function [parent1, parent2] = selection(population)

M = length(population.Chromosomes(:));

if any([population.Chromosomes(:).fitness] < 0 )

a = 1;

b = abs( min( [population.Chromosomes(:).fitness] ) );

Scaled_fitness = a * [population.Chromosomes(:).fitness] + b;

normalized_fitness = [Scaled_fitness] ./ sum([Scaled_fitness]);

else

normalized_fitness = [population.Chromosomes(:).fitness] ./ sum([population.Chromosomes(:).fitness]);

end

%normalized_fitness = [population.Chromosomes(:).fitness] ./ sum([population.Chromosomes(:).fitness]);

[sorted_fitness_values , sorted_idx] = sort(normalized_fitness , 'descend');

for i = 1 : length(population.Chromosomes)

temp_population.Chromosomes(i).Gene = population.Chromosomes(sorted_idx(i)).Gene;

temp_population.Chromosomes(i).fitness = population.Chromosomes(sorted_idx(i)).fitness;

temp_population.Chromosomes(i).normalized_fitness = normalized_fitness(sorted_idx(i));

end

cumsum = zeros(1,M);

for i = 1 : M

for j = i : M

cumsum(i) = cumsum(i) + temp_population.Chromosomes(j).normalized_fitness;

end

end

R = rand(); % in [0,1]

parent1_idx = M;

for i = 1: length(cumsum)

if R > cumsum(i)

parent1_idx = i - 1;

break;

end

end

parent2_idx = parent1_idx;

while_loop_stop = 0;

while parent2_idx == parent1_idx

while_loop_stop = while_loop_stop + 1;

R = rand(); % in [0,1]

if while_loop_stop > 20

break;

end

for i = 1: length(cumsum)

if R > cumsum(i)

parent2_idx = i - 1;

break;

end

end

end

parent1 = temp_population.Chromosomes(parent1_idx);

parent2 = temp_population.Chromosomes(parent2_idx);

end

Answer 1

[Star Strider]

It appears that you are using your own genetic algorithm code, rather than the ga function. The only problem is with the timeseries object.

There does not appear to be any way to calculate using timeseries objects (they appear to be data collection objects only, I have no experience with them), so it would likely be best to extract the data to a matrix and just use the matrix.