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Update algorithms folder #90

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merged 3 commits into from
May 20, 2024
Merged

Update algorithms folder #90

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265c79c
test.yml
Jafagervik Apr 20, 2024
5810c11
remove redundant inbounds
Jafagervik Apr 20, 2024
9475558
belongs to another pr
Jafagervik Apr 20, 2024
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8 changes: 4 additions & 4 deletions src/algorithms/ea.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ Returns a [`Result`](@ref).
"""
function oneplusone(f::Function, ind::AbstractVector, k_max::Integer, M::Mutator)
fx = Inf # works only on minimisation problems
runtime = @elapsed @inbounds for _ in 1:k_max
runtime = @elapsed for _ in 1:k_max
c = mutate(M, ind)
fx, fc = f(ind), f(c)
if fc <= fx
Expand All @@ -24,7 +24,7 @@ function oneplusone(f::Function, ind::AbstractVector, k_max::Integer, M::Mutator
end
end

n_evals = 2 * k_max
n_evals = 2k_max

return Result(fx, ind, [ind], k_max, n_evals, runtime)
end
Expand All @@ -34,7 +34,7 @@ function oneplusone!(
logger::Logbook, f::Function, ind::AbstractVector, k_max::Integer, M::Mutator
)
fx = Inf # works only on minimisation problems
runtime = @elapsed @inbounds for _ in 1:k_max
runtime = @elapsed for _ in 1:k_max
c = mutate(M, ind)
fx, fc = f(ind), f(c)
if fc <= fx # O(2 * k_max) # minimisation problem
Expand All @@ -45,7 +45,7 @@ function oneplusone!(
compute!(logger, [fx])
end

n_evals = 2 * k_max
n_evals = 2k_max

return Result(fx, ind, [ind], k_max, n_evals, runtime)
end
6 changes: 3 additions & 3 deletions src/algorithms/ga.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ function GA(

fitnesses = Vector{Float64}(undef, n)

runtime = @elapsed @inbounds for _ in 1:k_max
runtime = @elapsed for _ in 1:k_max
fitnesses = f.(population) # O(k_max * n)
parents = [select(S, fitnesses) for _ in eachindex(population)]
offspring = [cross(C, population[p[1]], population[p[2]]) for p in parents]
Expand Down Expand Up @@ -55,7 +55,7 @@ function GA!(

fitnesses = Vector{Float64}(undef, n)

runtime = @elapsed @inbounds for _ in 1:k_max
runtime = @elapsed for _ in 1:k_max
fitnesses = f.(population) # O(k_max * n)
parents = [select(S, fitnesses) for _ in eachindex(population)]
offspring = [cross(C, population[p[1]], population[p[2]]) for p in parents]
Expand Down Expand Up @@ -85,7 +85,7 @@ function GA!(

fitnesses = Vector{Float64}(undef, n)

runtime = @elapsed @inbounds for _ in 1:k_max
runtime = @elapsed for _ in 1:k_max
fitnesses = f.(population) # O(k_max * n)
parents = [select(S, fitnesses) for _ in eachindex(population)]
offspring = [cross(C, population[p[1]], population[p[2]]) for p in parents]
Expand Down
4 changes: 2 additions & 2 deletions src/algorithms/swarm.jl
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Expand Up @@ -51,7 +51,7 @@ function PSO(
for P in population
r1, r2 = rand(d), rand(d)
P.x += P.v
P.v = w * P.v + c1 * r1 .* (P.x_best - P.x) + c2 * r2 .* (x_best - P.x)
P.v = @fastmath w * P.v + c1 * r1 .* (P.x_best - P.x) + c2 * r2 .* (x_best - P.x)
P.y = f(P.x) # O(k_max * pop)

if P.y < y_best
Expand Down Expand Up @@ -93,7 +93,7 @@ function PSO!(
end
end

@inbounds for _ in 1:k_max
for _ in 1:k_max
for P in population
r1, r2 = rand(d), rand(d)
P.x += P.v
Expand Down
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