EMO changes (bug fixes), Tools change.

- Fixed bug in single point binary crossover where the returned
  population size failed when to_mate=None.
- LHS generator: the generator used the len of Problem.variables, which
  causes errors when problems have tensor valued variables. Now uses the
  number of flattened variables.
- Updated deprecated option in CbcOptions.

desdeo/emo/operators/crossover.py

@@ -279,6 +279,7 @@ def do(
 
         mating_pop = parent_decision_vars[shuffled_ids]
         mating_pop_size = len(shuffled_ids)
+        original_mating_pop_size = mating_pop_size
 
         if mating_pop_size % 2 != 0:
             # if the number of member to mate is of uneven size, copy the first member to the tail
@@ -317,7 +318,9 @@ def do(
         # combine the two offspring populations into one, drop the last member if the number of
         # indices (to_mate) is uneven
         self.offspring_population = pl.from_numpy(
-            np.vstack((offspring1, offspring2))[: (len(to_mate) if len(to_mate) % 2 == 0 else -1)],
+            np.vstack((offspring1, offspring2))[
+                : (original_mating_pop_size if original_mating_pop_size % 2 == 0 else -1)
+            ],
             schema=self.variable_symbols,
         ).select(pl.all().cast(pl.Float64))
         self.notify()

desdeo/emo/operators/generator.py

@@ -169,7 +169,7 @@ def __init__(self, problem: Problem, evaluator: EMOEvaluator, n_points: int, see
                 At the very least, the publisher argument should be provided.
         """
         super().__init__(problem, evaluator, n_points, seed, **kwargs)
-        self.lhsrng = LatinHypercube(d=len(problem.variables), seed=seed)
+        self.lhsrng = LatinHypercube(d=len(self.variable_symbols), seed=seed)
         self.seed = seed
 
     def do(self) -> tuple[pl.DataFrame, pl.DataFrame]:

desdeo/tools/pyomo_solver_interfaces.py

@@ -97,7 +97,7 @@ class CbcOptions(BaseModel):
         Please add options as they are needed and make a pull request.
     """
 
-    model_config = ConfigDict(frozen=True, allow_population_by_field_name=True)
+    model_config = ConfigDict(frozen=True, populate_by_name=True)
 
     sec: int = Field(
         description="The maximum amount of time (in seconds) the solver should run. Defaults to None.", default=None