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thechaingamer
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ada-git-code

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Task_number : integer;
Task_Load : integer;
type gene is array (integer range 1..gene_length) of integer range 1..gene_length;
type map_row is array (integer range 1..gene_length) of integer;
--use parent(i)(j) to access parent[i][j]
type container is array(integer range 1..population_size) of gene;
parent : container;
childarray :container;
--use map(i)(j) to access map[i][j]
map : array(integer range 1..gene_length) of map_row;
fitness : array(integer range 1..population_size) of integer;
--generate random map
procedure generate_map is
begin
for i in 1..gene_length loop
for j in i..gene_length loop
map(i)(j) := (random(g) mod max_dist-min_dist+1)+min_dist;
map(j)(i) := map(i)(j);
if i=j then
map(i)(j) := 0;
end if;
end loop;
end loop;
end generate_map;
--generate first generation
procedure generate_first is
rand_pos : integer;
temp :integer;
arr : gene;
begin
for i in 1..population_size loop
for j in 1..gene_length loop
arr(j):=j;
end loop;
for j in 1..gene_length loop
rand_pos := (random(g) mod (gene_length-j+1)) + 1;
parent(i)(j) := arr(rand_pos);
temp := arr(gene_length-j+1);
arr(gene_length-j+1) := arr(rand_pos);
arr(rand_pos) := temp;
end loop;
end loop;
end generate_first;
function selection return integer is
sum : integer := 0;
rand : integer;
begin
for i in 1..population_size loop
sum:=sum+10000/fitness(i);
end loop;
rand := (random(g) mod sum) + 1;
sum := 0;
for i in 1..population_size loop
sum:=sum+10000/fitness(i);
if sum >= rand then
return i;
end if;
end loop;
return (population_size);
end selection;
procedure crossover(c_i:integer) is
p:integer;
longest:integer:=1;
tmp:integer;
r1:integer;
r2:integer;
begin
p:=selection;
for i in 1..gene_length-1 loop
if map(parent(p)(i))(parent(p)(i+1)) > map(parent(p)(longest))(parent(p)(longest+1)) then
longest:=i;
end if;
end loop;
for i in 1..gene_length loop
if i + longest <= gene_length then
childarray(c_i)(i):=parent(p)(i+longest);
else
childarray(c_i)(i):=parent(p)((i+longest)mod gene_length);
end if;
end loop;
if random(g) mod mutation_rate = 1 then
r1:=random(g) mod gene_length +1;
r2:=random(g) mod gene_length +1;
tmp:=childarray(c_i)(r1);
childarray(c_i)(r1):=childarray(c_i)(r2);
childarray(c_i)(r2):=tmp;
end if;
end crossover;
procedure arrayAtoArrayB(A : in container;B : out container) is
begin
for i in 1 .. population_size loop
for j in 1 .. gene_length loop
B(i)(j) :=A(i)(j);
end loop;