symmetric_search_space(): deprecate

use RangePerDimSearchSpace instead

Author: alyst

examples/multiobjective.jl

@@ -11,7 +11,7 @@ res = bboptimize(schaffer1; Method=:borg_moea,
 # N is the number of the problem (not fitness) dimensions
 pareto_curve_func(t, ::Type{Val{N}}) where {N} = (N*t[1]^2, N*(2-t[1])^2)
 pareto_curve = BlackBoxOptim.Hypersurface(pareto_curve_func,
-                                          symmetric_search_space(1, (0.0, 2.0)))
+                                          RectSearchSpace(1, (0.0, 2.0)))
 
 # generate the set of ϵ-indexed points on the exact Pareto frontier
 pareto_pts = BlackBoxOptim.generate(pareto_curve,

src/BlackBoxOptim.jl

@@ -60,7 +60,7 @@ export  Optimizer, AskTellOptimizer, SteppingOptimizer, PopulationOptimizer,
 
         # Search spaces
         ParamBounds, Individual, SearchSpace, FixedDimensionSearchSpace,
-        RectSearchSpace, ContinuousRectSearchSpace, symmetric_search_space,
+        RectSearchSpace, ContinuousRectSearchSpace,
         numdims, dimmin, dimmax, dimdelta, dimrange,
         rand_individual, rand_individuals, rand_individuals_lhs,
 

src/adaptive_differential_evolution.jl

@@ -4,7 +4,7 @@ const ADE_DefaultOptions = chain(DE_DefaultOptions, ParamsDict(
     # Distributions we will use to generate new F and CR values.
     :fdistr => BimodalCauchy(0.65, 0.1, 1.0, 0.1, clampBelow0 = false),
     :crdistr => BimodalCauchy(0.1, 0.1, 0.95, 0.1, clampBelow0 = false),
-    :SearchSpace => symmetric_search_space(1)
+    :SearchSpace => RectSearchSpace(1)
 ))
 
 """

src/default_parameters.jl

@@ -64,7 +64,7 @@ function check_and_create_search_space(params::Parameters)
             if ndim == :NotSpecified
                 throw(ArgumentError("You MUST specify NumDimensions= in a solution when giving a SearchRange=$(sr)"))
             end
-            return symmetric_search_space(params[:NumDimensions], sr)
+            return RectSearchSpace(params[:NumDimensions], sr)
         elseif isa(sr, typeof([(0.0, 1.0)]))
             return RectSearchSpace(sr)
         else

src/problems/multi_objective.jl

@@ -148,13 +148,13 @@ See http://dces.essex.ac.uk/staff/zhang/MOEAcompetition/cec09testproblem0904.pdf
 const CEC09_Unconstrained_Set = Dict{Int,FunctionBasedProblemFamily}(
     8 => FunctionBasedProblemFamily(CEC09_UP8, "CEC09 UP8",  ParetoFitnessScheme{3}(is_minimizing=true),
                                     (-2.0, 2.0),
-                                    Hypersurface(CEC09_UP8_PF, symmetric_search_space(2, (0.0, 1.0))),
-                                    symmetric_search_space(2, (0.0, 1.0)))
+                                    Hypersurface(CEC09_UP8_PF, RectSearchSpace(2, (0.0, 1.0))),
+                                    RectSearchSpace(2, (0.0, 1.0)))
 )
 
 schaffer1(x) = (sum(abs2, x), sum(xx -> abs2(xx - 2.0), x))
 schaffer1_PF(t, ::Type{Val{NP}}) where {NP} = (NP*t[1]^2, NP*(2-t[1])^2)
 
 const Schaffer1Family = FunctionBasedProblemFamily(schaffer1, "Schaffer1", ParetoFitnessScheme{2}(is_minimizing=true),
                                 (-10.0, 10.0),
-                                Hypersurface(schaffer1_PF, symmetric_search_space(1, (0.0, 2.0))))
+                                Hypersurface(schaffer1_PF, RectSearchSpace(1, (0.0, 2.0))))

src/problems/problem_family.jl

@@ -44,7 +44,7 @@ Construct search space for `FunctionBasedProblem` with the given number of dimen
 function instantiate_search_space(family::FunctionBasedProblemFamily, ndim::Int)
     ndim >= numdims(family.reserved_ss) ||
         throw(ArgumentError("Cannot create $ndim-problem: number of dimensions less than reserved dimensions"))
-    vcat(family.reserved_ss, symmetric_search_space(ndim - numdims(family.reserved_ss), family.range_per_dim))
+    vcat(family.reserved_ss, RectSearchSpace(ndim - numdims(family.reserved_ss), family.range_per_dim))
 end
 
 """

src/search_space.jl

@@ -179,5 +179,7 @@ RectSearchSpace(ranges::AbstractVector) =
 Create `RectSearchSpace` with given number of dimensions
 and given range of valid values for each dimension.
 """
-symmetric_search_space(numdims::Integer, range=(0.0, 1.0)) =
+RectSearchSpace(numdims::Integer, range=(0.0, 1.0)) =
     RectSearchSpace(fill(range, numdims))
+
+@deprecate symmetric_search_space(numdims, range=(0.0, 1.0)) RectSearchSpace(numdims, range)

test/problems/test_problem.jl

@@ -9,7 +9,7 @@ fsum_abs_and_sq(x) = (sum(abs, x), sum(abs2, x))
     @test fitness_scheme(p) == MinimizingFitnessScheme
     @test numobjectives(p) == 1
     @test numdims(p) == 1
-    @test search_space(p) == symmetric_search_space(1, (-1.0, 1.0))
+    @test search_space(p) == RectSearchSpace(1, (-1.0, 1.0))
 
     @test fitness([0.0], p) == 0.0
     @test fitness([1.2], p) == 1.2
@@ -22,14 +22,14 @@ end
     @test fitness_scheme(p) == MinimizingFitnessScheme
     @test numobjectives(p) == 1
     @test numdims(p) == 3
-    @test search_space(p) == symmetric_search_space(3, (-1.0, 1.0))
+    @test search_space(p) == RectSearchSpace(3, (-1.0, 1.0))
 
     @test fitness([0.0, 1.0, 2.0], p) == 3.0
     @test fitness([-1.0, 1.0, 2.0], p) == 4.0
 end
 
 @testset "1-dimensional, multi-objective sumabs_sumsq" begin
-    ss = symmetric_search_space(1)
+    ss = RectSearchSpace(1)
     p = FunctionBasedProblem(fsum_abs_and_sq, "sumabs_sumsq",
                              ParetoFitnessScheme{2}(), ss, (0.0, 0.0))
 
@@ -73,7 +73,7 @@ end
     @test BlackBoxOptim.orig_problem(sp) === p
     @test numobjectives(sp) == 1
     @test numdims(sp) == 1
-    @test search_space(sp) == symmetric_search_space(1, (-1.0, 1.0))
+    @test search_space(sp) == RectSearchSpace(1, (-1.0, 1.0))
     xs = sp.xshift
     @test xs == [0.5]
     @test sp.fitshift == 0.0
@@ -84,7 +84,7 @@ end
 end
 
 @testset "Shifted and biased 2-dim" begin
-    ss = symmetric_search_space(2, (-0.5, 1.0))
+    ss = RectSearchSpace(2, (-0.5, 1.0))
     p = minimization_problem(fsabs, "sumabs", (-0.5, 1.0), 2, 0.0)
     sp = BlackBoxOptim.ShiftedAndBiasedProblem(p; fitshift = 1.3)
 
@@ -93,7 +93,7 @@ end
     @test numdims(sp) == 2
     @test sp.xshift == [0.0, 0.0]
     @test sp.fitshift == 1.3
-    @test search_space(sp) == symmetric_search_space(2, (-0.5, 1.0))
+    @test search_space(sp) == RectSearchSpace(2, (-0.5, 1.0))
 
     xs = sp.xshift
     @test fitness([0.0, 1.0], sp) == 1.0 + 1.3

test/test_adaptive_differential_evolution.jl

@@ -2,7 +2,7 @@ NumTestRepetitions = 100
 
 @testset "Adaptive differential evolution optimizer" begin
 
-ss = symmetric_search_space(1, (0.0, 10.0))
+ss = RectSearchSpace(1, (0.0, 10.0))
 fake_problem = FunctionBasedProblem(x -> 0.0, "test_problem", MinimizingFitnessScheme, ss)
 
 ade = adaptive_de_rand_1_bin(fake_problem, ParamsDict(

test/test_crossover_operators.jl

@@ -1,6 +1,6 @@
 @testset "Crossover operators" begin
 
-ss = symmetric_search_space(1, (0.0, 10.0))
+ss = RectSearchSpace(1, (0.0, 10.0))
 fake_problem = FunctionBasedProblem(x -> 0.0, "test_problem", MinimizingFitnessScheme, ss)
 DE = de_rand_1_bin(fake_problem, ParamsDict(
     :Population => reshape(collect(1.0:10.0), (1, 10)),
@@ -63,7 +63,7 @@ DE = de_rand_1_bin(fake_problem, ParamsDict(
 end
 
 @testset "MutationWrapper" begin
-    ss = symmetric_search_space(2, (0.0, 10.0))
+    ss = RectSearchSpace(2, (0.0, 10.0))
     pop = reshape(collect(1.0:8.0), 2, 4)
     gibbs = UniformMutation(ss)
     gibbs_wrapper = BlackBoxOptim.MutationWrapper(gibbs)

test/test_differential_evolution.jl

@@ -1,6 +1,6 @@
 @testset "Differential evolution optimizer" begin
 
-ss = symmetric_search_space(1, (0.0, 10.0))
+ss = RectSearchSpace(1, (0.0, 10.0))
 fake_problem = FunctionBasedProblem(x -> 0.0, "test_problem", MinimizingFitnessScheme, ss)
 DE = de_rand_1_bin(fake_problem, ParamsDict(
     :Population => reshape(collect(1.0:10.0), (1, 10)),

test/test_generating_set_search.jl

@@ -1,9 +1,9 @@
 @testset "Generating set search" begin
 
-    ss = symmetric_search_space(3, (0.0, 1.0))
+    ss = RectSearchSpace(3, (0.0, 1.0))
     @test BlackBoxOptim.calc_initial_step_size(ss) == (0.5 * (1.0 - 0.0))
 
-    ss = symmetric_search_space(3, (-1.2, 42.0))
+    ss = RectSearchSpace(3, (-1.2, 42.0))
     @test BlackBoxOptim.calc_initial_step_size(ss, 0.80) == (0.80 * (42.0 + 1.2))  
 
 end

test/test_random_search.jl

@@ -6,7 +6,7 @@
         min = rand(1:123)
         range = (min * rand(), min + rand() * min)
 
-        ss = BlackBoxOptim.symmetric_search_space(dims, range)
+        ss = RectSearchSpace(dims, range)
         opt = BlackBoxOptim.random_search(ss)
 
         res1 = BlackBoxOptim.ask(opt)

test/test_search_space.jl

@@ -2,7 +2,7 @@
     @testset "in()" begin
         for i in 1:NumTestRepetitions
             reps = rand(1:10)
-            ss1 = symmetric_search_space(reps, (0.0, 1.0))
+            ss1 = RectSearchSpace(reps, (0.0, 1.0))
             ind = rand_individual(ss1)
             for j in 1:reps
                 @test (dimmin(ss1, j) <= ind[j] <= dimmax(ss1, j))
@@ -11,7 +11,7 @@
     end
 
     @testset "Symmetric search space with default range" begin
-        ss1 = symmetric_search_space(1)
+        ss1 = RectSearchSpace(1)
         @test numdims(ss1) == 1
         @test dimmin(ss1) == [0.0]
         @test dimmin(ss1, 1) == 0.0
@@ -28,7 +28,7 @@
             @test in(ind, ss1)
         end
 
-        ss3 = symmetric_search_space(3)
+        ss3 = RectSearchSpace(3)
         @test numdims(ss3) == 3
         @test dimmin(ss3) == [0.0, 0.0, 0.0]
         @test dimmin(ss3, 2) == 0.0
@@ -49,8 +49,8 @@
     end
 
     @testset "ContinuousRectSearchSpace with given range" begin
-        ss1 = symmetric_search_space(1, (-1.0, 1.0))
-        @test_throws ArgumentError symmetric_search_space(1, (0.0, -1.0))
+        ss1 = RectSearchSpace(1, (-1.0, 1.0))
+        @test_throws ArgumentError RectSearchSpace(1, (0.0, -1.0))
         @test ss1 isa ContinuousRectSearchSpace
         @test numdims(ss1) == 1
         @test dimrange(ss1) == [(-1.0, 1.0)]
@@ -60,7 +60,7 @@
             reps = rand(1:100)
             a = rand()
             range = (a, a + (1-a)*rand())
-            ss = symmetric_search_space(reps, range)
+            ss = RectSearchSpace(reps, range)
             @test numdims(ss) == reps
             @test all(dr -> dr == range, dimrange(ss))
         end
@@ -71,7 +71,7 @@
             reps = rand(1:100)
             mm = sort(rand(2,1), dims=1)
             range = (mm[1], mm[2])
-            ss = symmetric_search_space(reps, range)
+            ss = RectSearchSpace(reps, range)
             ind = rand_individual(ss)
             @test length(ind) == numdims(ss)
             @test in(ind, ss)
@@ -83,7 +83,7 @@
             reps = rand(1:10)
             mm = sort(rand(2,1), dims=1)
             range = (mm[1], mm[2])
-            ss = symmetric_search_space(reps, range)
+            ss = RectSearchSpace(reps, range)
             numinds = rand(1:10)
             inds = rand_individuals(ss, numinds)
             @test size(inds,1) == numdims(ss)

test/test_selectors.jl

@@ -1,6 +1,6 @@
 @testset "Selection operators" begin
 
-ss = symmetric_search_space(1, (0.0, 10.0))
+ss = RectSearchSpace(1, (0.0, 10.0))
 fake_problem = FunctionBasedProblem(x -> 0.0, "test_problem", MinimizingFitnessScheme, ss)
 fake_pop = reshape(collect(1.0:10.0), (1, 10))