Three examples tested

examples/nest_maintainence/experiment.py

@@ -124,7 +124,8 @@ def simulate(env, iteration, N=100):
     threshold = 1.0
 
     sim = SimModel(
-        N, 100, 100, 10, seed=None, iter=iteration, xmlstrings=phenotypes, pname=env[1])
+        N, 100, 100, 10, seed=None, iter=iteration,
+        xmlstrings=phenotypes, pname=env[1])
     sim.build_environment_from_json()
 
     # print(len(sim.obstacles), len(sim.debris))
@@ -181,7 +182,8 @@ def main(iter):
     """Block for the main function."""
     print('=======Start=========')
     # env = evolve(iter)
-    pname = '/home/aadeshnpn/Documents/BYU/hcmi/hri/nest_maint/10000NestMAgents'
+    pname = (
+        '/home/aadeshnpn/Documents/BYU/hcmi/hri/nest_maint/10000NestMAgents')
     jfilename = pname + '/1539014820252.json'
     jdata = JsonPhenotypeData.load_json_file(jfilename)
     phenotypes = jdata['phenotypes']

examples/nest_maintainence_evolution/experiment.py

@@ -7,8 +7,8 @@
 from swarms.utils.results import SimulationResults
 # import py_trees
 # Global variables for width and height
-width = 100
-height = 100
+width = 200
+height = 200
 
 UI = False
 
@@ -112,7 +112,7 @@ def learning_phase(iteration, early_stop=False):
                 phenotype_to_json(
                     env.pname, env.runid, phenotypes)
                 validation_loop(
-                    phenotypes, 5000, phenotypes)
+                    phenotypes, 5000, parentname=env.pname)
             except ValueError:
                 pass
             # Plot the fitness in the graph
@@ -173,7 +173,7 @@ def test_json_phenotype(json):
     # Running 50 experiments in parallel
     # Parallel(n_jobs=8)(delayed(main)(i) for i in range(2000, 100000, 2000))
     # Parallel(n_jobs=4)(delayed(main)(i) for i in range(1000, 8000, 2000))
-    # main(8000)
+    main(8000)
     # json = '1543367322976111-5999.json'
     # test_json_phenotype(json)
-    Parallel(n_jobs=4)(delayed(main)(8000) for i in range(128))
+    # Parallel(n_jobs=4)(delayed(main)(8000) for i in range(128))

examples/nest_maintainence_evolution/model.py

@@ -181,7 +181,7 @@ def gather_info(self):
         for id in range(len(self.agents)):
             # diversity[id] = self.agents[id].diversity_fitness
             exploration[id] = self.agents[id].exploration_fitness()
-            foraging[id] = self.agents[id].food_collected
+            foraging[id] = self.agents[id].debris_collected
             fittest[id] = self.agents[id].individual[0].fitness
             prospective[id] = self.agents[id].carrying_fitness()
 
@@ -213,7 +213,7 @@ def best_agents(self, data, beta, header):
         idx = np.argmax(data)
         # dfitness = self.agents[idx].diversity_fitness
         ofitness = self.agents[idx].individual[0].fitness
-        ffitness = self.agents[idx].food_collected
+        ffitness = self.agents[idx].debris_collected
         efitness = self.agents[idx].exploration_fitness()
         pfitness = self.agents[idx].carrying_fitness()
         phenotype = self.agents[idx].individual[0].phenotype

grammars/sf.bnf

@@ -0,0 +1,11 @@
+<s> ::= [?xml version=%1.0% encoding=%UTF-8%?]<cf>
+<cf> ::= <sequence> | <selector>
+<sequence> ::= [Sequence]<execution>[/Sequence] | [Sequence]<cf><cf>[/Sequence] | [Sequence]<sequence> <cf>[/Sequence]
+<selector> ::= [Selector]<execution>[/Selector] | [Selector]<cf><cf>[/Selector] | [Selector]<selector> <cf>[/Selector]
+<execution>::=<conditions>[act]<action>[/act]
+<conditions>::=[cond]<condition>[/cond]<conditions>|[cond]<condition>[/cond]
+<condition>::=NeighbourObjects|IsDropable_<sobjects>|NeighbourObjects_<objects>|NeighbourObjects_<objects>_invert|IsVisitedBefore_<sobjects>|IsVisitedBefore_<sobjects>_invert|IsCarrying_<dobjects>|IsCarrying_<dobjects>_invert
+<action>::=MoveTowards_<sobjects>|Explore|CompositeSingleCarry_<dobjects>|CompositeDrop_<dobjects>|MoveAway_<sobjects>
+<sobjects>::=Hub|Sites
+<dobjects>::=Food
+<objects>::=<sobjects>|<dobjects>

parameters/nm.txt

@@ -1,5 +1,5 @@
 CACHE:                  True
-CODON_SIZE:             1000
+CODON_SIZE:             2000
 CROSSOVER:              variable_onepoint
 CROSSOVER_PROBABILITY:  0.9
 DEBUG:                  False
@@ -8,10 +8,10 @@ MAX_GENOME_LENGTH:      50
 GRAMMAR_FILE:           ../..,nm.bnf
 INITIALISATION:         uniform_tree
 INVALID_SELECTION:      False
-MAX_INIT_TREE_DEPTH:    5
-MAX_TREE_DEPTH:         12
+MAX_INIT_TREE_DEPTH:    7
+MAX_TREE_DEPTH:         10
 MUTATION:               int_flip_per_codon
-MUTATION_PROBABILITY:   0.09
+MUTATION_PROBABILITY:   0.1
 FITNESS_FUNCTION:       swarm.bt_diversity
 REPLACEMENT:            generational
 SELECTION:              truncation