diff --git a/moptipy/algorithms/so/ffa/fea1plus1.py b/moptipy/algorithms/so/ffa/fea1plus1.py
index 3b757e657..b43347091 100644
--- a/moptipy/algorithms/so/ffa/fea1plus1.py
+++ b/moptipy/algorithms/so/ffa/fea1plus1.py
@@ -53,7 +53,7 @@
 
 FFA is also implemented as a fitness assignment process
 (:mod:`~moptipy.algorithms.so.fitness`) in module
-:mod:`~moptipy.algorithms.so.fitnesses.ffa`.
+:mod:`~moptipy.algorithms.so.ffa.ffa_fitness`.
 
 1. Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness
    Assignment: Making Optimization Algorithms Invariant under Bijective
@@ -231,7 +231,7 @@ class FEA1plus1(Algorithm1):
     away from each other. A more general version is available as a fitness
     assignment process (:mod:`~moptipy.algorithms.so.fitness`) that can
     be plugged into a general EA (:mod:`~moptipy.algorithms.so.general_ea`)
-    in module :mod:`~moptipy.algorithms.so.fitnesses.ffa`.
+    in module :mod:`~moptipy.algorithms.so.ffa.ffa_fitness`.
     """
 
     def __init__(self, op0: Op0, op1: Op1, log_h_tbl: bool = True) -> None:
diff --git a/moptipy/algorithms/so/ffa/ffa_fitness.py b/moptipy/algorithms/so/ffa/ffa_fitness.py
index 1ed37ffe3..8af67fb89 100644
--- a/moptipy/algorithms/so/ffa/ffa_fitness.py
+++ b/moptipy/algorithms/so/ffa/ffa_fitness.py
@@ -13,7 +13,7 @@
 This index is used to break ties, in which case newer solutions are preferred.
 
 This can make the EA with FFA compatible with the
-:class:`moptipy.algorithms.so.fea1plus1.FEA1plus1` if "best" selection
+:class:`moptipy.algorithms.so.ffa.fea1plus1.FEA1plus1` if "best" selection
 (:class:`moptipy.algorithms.modules.selections.best.Best`) is used
 at mu=lambda=1.
 To facilitate this, there is one special case in the FFA fitness assignment:
diff --git a/moptipy/evaluation/frequency.py b/moptipy/evaluation/frequency.py
index e33b133a3..d95c381fa 100644
--- a/moptipy/evaluation/frequency.py
+++ b/moptipy/evaluation/frequency.py
@@ -78,8 +78,9 @@ def from_logs(
     of at least `1` if they are present in the log files (in the `SETUP`
     section).
     If `report_h` is `True`, then a frequency fitness assignment `H` section
-    is parsed, if present (see :mod:`~moptipy.algorithms.so.fea1plus1`). Such
-    a section contains tuples of objective values and encounter frequencies.
+    is parsed, if present (see :mod:`~moptipy.algorithms.so.ffa.fea1plus1`).
+    Such a section contains tuples of objective values and encounter
+    frequencies.
     These encounter frequencies are added to the counter. This means that if
     you set *both* `report_progress` and `report_h` to `True`, you will get
     frequencies that are too high.
@@ -92,7 +93,7 @@ def from_logs(
     values, we could log *all FEs* to the log files and set `report_progress`
     to `True` and everything else to `False`. Then we get correct encounter
     frequencies. Alternatively, if we have a purly FFA-based algorithm (see,
-    again, :mod:`~moptipy.algorithms.so.fea1plus1`), then we can set
+    again, :mod:`~moptipy.algorithms.so.ffa.fea1plus1`), then we can set
     `report_progress` to `True` and everything else to `False` to get a
     similar result, but the encounter frequencies then depend on the selection
     scheme. Alternatively, if we only care about whether an objective value