Update general_usage.md

Extend the general usage docs to mention the status object

Author: engintoklu

docs/user_guide/general_usage.md

@@ -40,6 +40,58 @@ searcher.run(10)
 # Process the information accumulated by the loggers.
 ...
 progress = pandas_logger.to_dataframe()
-progress.mean_eval.plot()  # Display a graph of the evolutionary progress by using the pandas data frame
+progress.mean_eval.plot()  # Plot the evolutionary progress
 ...
+
+# We now analyze the current status using the dictionary-like status object.
+# The status object allows one to get evaluation results (i.e. fitness, etc.)
+# and decision values of the best solution in the population
+# (via the key "pop_best"), center of the search distribution
+# (via the key "center", in case a distribution-based evolutionary
+# algorithm is being used), etc.
+
+for status_key in searcher.iter_status_keys():
+    print("===", status_key, "===")
+    print(searcher.status[status_key])
+    print()
+
+
+# === Further ways of analyzing the results of the evolutionary run: ===
+
+
+# If a distribution-based algorithm is used (e.g. SNES, XNES, CEM, PGPE,
+# CMAES), the status object includes an item with key "center", representing
+# the center (i.e. mean) of the search distribution as a read-only tensor.
+#
+# center_point_as_tensor = searcher.status["center"]
+
+
+# Algorithms such as Cosyne, GeneticAlgorithm, etc. do not have
+# a search distribution, therefore, they do not have a center point.
+# However, the best solution of their last population can be obtained
+# via the status key "pop_best", as an instance of `evotorch.Solution`.
+#
+# solution_object = searcher.status["pop_best"]
+# decision_values_as_tensor = solution_object.values
+# evals_as_tensor = solution_object.evals  # fitness(es), evaluation data, etc.
+
+
+# If the Problem object was initialized with `store_solution_stats=True`
+# (which is enabled by default when the device of the Problem is "cpu"),
+# the solution with the best fitness ever observed is available via the
+# status key "best".
+#
+# best_sln = searcher.status["best"]
+# best_sln_decision_values = best_solution.values
+# best_sln_evals = best_sln.evals  # fitness(s), evaluation data, etc.
+
+
+# Unless the search algorithm was initialized to work across remote
+# actors (via `distributed=True`), the search algorithm keeps its last
+# population accessible via the attribute named `population`.
+#
+# for solution in searcher.population:
+#     print("Decision values:", solution.values)
+#     print("Evaluation:", solution.evals)  # fitnesses, evaluation data, etc.
+#     print()
 ```