Changeset 1272 for framspy/evolalg/base/experiment_niching_abc.py

Timestamp:

08/05/23 00:58:45 (10 months ago)

Author:

Maciej Komosinski

Message:

Added comments, formatting

File:

• framspy/evolalg/base/experiment_niching_abc.py (modified) (3 diffs)

framspy/evolalg/base/experiment_niching_abc.py

@@ -141 +141 @@
             newpop.append(Individual().copyFrom(ind))
 
-        pop_offspring = population+newpop
+        pop_offspring = population + newpop # this is OK for NSGA2, but TODO verify if this should also be used for NSLC? 
         print(len(pop_offspring))
-        if self.fit == "nslc":
+        if self.fit == "nslc": # TODO should NSLC be also equipped with a novelty archive? (with an admittance threshold?)
             self.do_nslc_dissim(pop_offspring)
         elif self.fit == "nsga2":
@@ -156 +156 @@
             # saved generation has been completed, start with the next one
             self.current_generation += 1
-            print("...Resuming from saved state: population size = %d, hof size = %d, stats size = %d, archive size = %d, generation = %d/%d" % (len(self.population_structures.population), len(self.hof),
-                                                                                                                                                 len(self.stats),  (len(self.population_structures.archive)), self.current_generation, generations))  # self.current_generation (and g) are 0-based, parsed_args.generations is 1-based
+            print("...Resuming from saved state: population size = %d, hof size = %d, stats size = %d, archive size = %d, generation = %d/%d" % (len(self.population_structures.population), len(self.hof), len(self.stats), (len(self.population_structures.archive)), self.current_generation, generations))  # self.current_generation (and g) are 0-based, parsed_args.generations is 1-based
         else:
             self.initialize_evolution(self.genformat, initialgenotype)
@@ -190 +189 @@
         parser.add_argument("-fit",type= str, default="raw",
                         help="Fitness type, availible types: niching, novelty, knn_niching (local), knn_novelty (local), nsga2, nslc and raw (default)")
-        parser.add_argument("-archive",type= int, default=50,
-                            help="Maximum archive size")
+        parser.add_argument("-archive",type= int, default=50, help="Maximum archive size")
         parser.add_argument("-normalize",type= str, default= "max",
                             help="What normalization to use for the dissimilarity matrix: max (default}, sum, or none")