rename OptimalFitness to TargetFitness

also some minor code cleanups

README.md

@@ -110,7 +110,7 @@ In addition to the `Method` parameter, there are many other parameters you can c
 * `MaxFuncEvals`: How many evaluations that are allowed of the function being optimized.
 * `TraceMode`: How optimization progress should be displayed (`:silent`, `:compact`, `:verbose`). Defaults to `:compact` that outputs current number of fitness evaluations and best value each `TraceInterval` seconds.
 * `PopulationSize`: How large is the initial population for population-based optimizers? Defaults to `50`.
-* `OptimalFitness`. Allows to specify the value of the best fitness for a given problem. The algorithm stops as soon as the distance between the current `best_fitness()` and `OptimalFitness` is less than `FitnessTolerance`.
+* `TargetFitness`. Allows to specify the value of the best fitness for a given problem. The algorithm stops as soon as the distance between the current `best_fitness()` and `TargetFitness` is less than `FitnessTolerance`.
 This list is not complete though, please refer to the `examples` and `tests` directories for additional examples.
 
 # State of the Library

src/bboptimize.jl

@@ -29,7 +29,7 @@ function setup_problem(func::Function, parameters::Parameters)
   # Now create an optimization problem with the given information. We currently reuse the type
   # from our pre-defined problems so some of the data for the constructor is dummy.
   problem = FunctionBasedProblem(func, "<unknown>", parameters[:FitnessScheme], ss,
-                                 parameters[:OptimalFitness])
+                                 parameters[:TargetFitness])
 
   # validate fitness: create a random solution from the search space and ensure that fitness(problem) returns fitness_type(problem).
   ind = rand_individual(search_space(problem))

src/default_parameters.jl

@@ -8,7 +8,7 @@ const DefaultParameters = ParamsDict(
   :SearchRange    => (-1.0, 1.0), # Default search range to use per dimension unless specified
   :SearchSpace    => false, # Search space can be directly specified and will then take precedence over NumDimensions and SearchRange.
   :FitnessScheme  => MinimizingFitnessScheme, # fitness scheme to be used
-  :OptimalFitness => nothing, # optimal (target) fitness, if known
+  :TargetFitness => nothing, # optimal (target) fitness, if known
 
   :Method => :adaptive_de_rand_1_bin_radiuslimited,
 

test/test_toplevel_bboptimize.jl

@@ -145,12 +145,12 @@ end
     end
   end
 
-  context("OptimalFitness option works") do
+  context("TargetFitness option works") do
     # FIXME use the same (fixed?) random seed to guarantee reproducibility
     result1 = bboptimize(rosenbrock, SearchRange = (-5.0, 5.0), NumDimensions = 5,
                          Method = :de_rand_1_bin, FitnessTolerance = 1e-5,
                          MaxSteps = 1000000, TraceMode = :silent,
-                         OptimalFitness = 0.0)
+                         TargetFitness = 0.0)
     result2 = bboptimize(rosenbrock, SearchRange = (-5.0, 5.0), NumDimensions = 5,
                          Method = :de_rand_1_bin, FitnessTolerance = 1e-5,
                          MaxSteps = 1000000, TraceMode = :silent)