some typos in comments and 1 variable fixed

src/agent/agent.py

@@ -18,13 +18,13 @@ def __init__(self, ip):
         # Interaction probability received in constructor
         self.interaction_probability = ip
 
-        # Only initialize singel individual. Single agent can only have single genetic information
+        # Only initialize single individual. Single agent can only have single genetic information
         self.individual = initialisation(1)   
 
         # Evaluate the fitness for the the individual    
         self.individual = evaluate_fitness(self.individual)
 
-        # Flag which store the boolean value for other nebouring agents found or not
+        # Flag which store the boolean value for other neighbouring agents found or not
         self.agents_found = False
 
 
@@ -83,12 +83,12 @@ def act(self):
             # Sort the individuals list 
             individuals.sort(reverse=True)
 
-            # Get the higest performing individual from the sorted population 
+            # Get the highest performing individual from the sorted population
             self.new_individual = individuals[0]
 
     def update(self):
         #Update the information if the agent has sense nearby agents
         if self.agents_found:
 
-            # Repalce the individual with the higest performing individual obtained from act method
+            # Replace the individual with the highest performing individual obtained from act method
             self.individual = [self.new_individual]

src/algorithm/distributed_algorithm/search_loop.py

@@ -15,9 +15,9 @@ def create_agents(n,p):
 
 def search_loop():
     """
-    This loop is used when the multiagent parameter is passed
+    This loop is used when the multi-agent parameter is passed
     """
-    # Create a list of agents based on the paramater passed
+    # Create a list of agents based on the parameter passed
     agents = create_agents(params['AGENT_SIZE'],params['INTERACTION_PROBABILITY'])
 
     ##Multi-Agent based GE

src/algorithm/hill_climbing.py

@@ -4,7 +4,7 @@
 from utilities.stats import trackers
 
 
-"""Hill-climbing is just about the simplest metaheuristic there
+"""Hill-climbing is just about the simplest meta-heuristic there
 is. It's of interest in GP/GE because of the lingering suspicion among
 many researchers that crossover just doesn't work. This goes back to
 90s work by Chellapilla and by O'Reilly. Today, many papers are

src/algorithm/mapper.py

@@ -82,7 +82,7 @@ def map_ind_from_genome(genome):
              The number of used codons.
     """
 
-    # Create local variables to avoide multiple dictionary lookups
+    # Create local variables to avoid multiple dictionary lookups
     max_tree_depth, max_wraps = params['MAX_TREE_DEPTH'], params['MAX_WRAPS']
     bnf_grammar = params['BNF_GRAMMAR']
 

src/algorithm/parameters.py

@@ -176,12 +176,12 @@
         # encourage diversity in the population.
         'MUTATE_DUPLICATES': False,
 
-        # MULTIAGENT Parameters
-        # True or False for Multiagent
+        # MULTI-AGENT Parameters
+        # True or False for multi-agent
         'MULTIAGENT': False,
         # Agent Size. Number of agents having their own copy of genetic material
         'AGENT_SIZE': 100,
-        # Interaction Probablity. How frequently the agents can interaction with each other
+        # Interaction Probability. How frequently the agents can interaction with each other
         'INTERACTION_PROBABILITY': 0.5,
 
         # OTHER
@@ -201,7 +201,7 @@ def load_params(file_name):
     try:
         open(file_name, "r")
     except FileNotFoundError:
-        s = "algorithm.paremeters.load_params\n" \
+        s = "algorithm.parameters.load_params\n" \
             "Error: Parameters file not found.\n" \
             "       Ensure file extension is specified, e.g. 'regression.txt'."
         raise Exception(s)

src/algorithm/search_loop.py

@@ -16,7 +16,7 @@ def search_loop():
     """
 
     if params['MULTICORE']:
-        # initialize pool once, if mutlicore is enabled
+        # initialize pool once, if multi-core is enabled
         params['POOL'] = Pool(processes=params['CORES'], initializer=pool_init,
                               initargs=(params,))  # , maxtasksperchild=1)
 
@@ -55,7 +55,7 @@ def search_loop_from_state():
     individuals = trackers.state_individuals
 
     if params['MULTICORE']:
-        # initialize pool once, if mutlicore is enabled
+        # initialize pool once, if multi-core is enabled
         params['POOL'] = Pool(processes=params['CORES'], initializer=pool_init,
                               initargs=(params,))  # , maxtasksperchild=1)
 

src/fitness/base_ff_classes/moo_ff.py

@@ -66,7 +66,7 @@ def value(fitness_vector, objective_index):
         
         :param fitness_vector: A vector/list of fitnesses.
         :param objective_index: The index of the desired fitness.
-        :return: The fitness at the objective index of the fitness vecror.
+        :return: The fitness at the objective index of the fitness vector.
         """
 
         if not isinstance(fitness_vector, list):

src/fitness/multi_objective/singlefit_multiobj.py

@@ -11,7 +11,7 @@
 class singlefit_multiobj(base_ff):
     """
     An example of a single fitness class that generates
-    two fitness values for multiobjective optimisation
+    two fitness values for multi-objective optimisation
     """
 
     maximise = True
@@ -57,7 +57,7 @@ def value(fitness_vector, objective_index):
 
         :param fitness_vector: A vector/list of fitnesses.
         :param objective_index: The index of the desired fitness.
-        :return: The fitness at the objective index of the fitness vecror.
+        :return: The fitness at the objective index of the fitness vector.
         """
 
         if not isinstance(fitness_vector, list):

src/fitness/progsys.py

@@ -33,7 +33,7 @@ def __init__(self):
                           params['GRAMMAR_FILE'])
         self.eval = self.create_eval_process()
         if params['MULTICORE']:
-            print("Warming: Multicore is not supported with progsys "
+            print("Warming: multi-core is not supported with progsys "
                   "as fitness function.\n"
                   "Fitness function only allows sequential evaluation.")
 

src/fitness/regex/RegexEval.py

@@ -32,7 +32,7 @@ def __init__(self):
 
         if params['MULTICORE']:
             s = "fitness.regex.RegexEval.RegexEval\n" \
-                "Error: Multicore evaluation cannot be used with RegexEval " \
+                "Error: multi-core evaluation cannot be used with RegexEval " \
                 "fitness function, as this fitness function already manages " \
                 "processes using the multiprocessing library."
             raise Exception(s)

src/fitness/sequence_match.py

@@ -124,7 +124,7 @@ def compress(s):
 
 def compressibility(s):
     """
-    Compressability is in [0, 1]. It's high when the compressed string
+    Compressibility is in [0, 1]. It's high when the compressed string
     is much shorter than the original.
     
     :param s:

src/operators/subtree_parse.py

@@ -30,7 +30,7 @@ def combine_snippets():
     # Get new snippets list.
     updated_snippets = sorted(trackers.snippets.keys())
 
-    # Initialise counter for reduction interations.
+    # Initialise counter for reduction iterations.
     no_passes = 1
 
     while updated_snippets != original_snippets:
@@ -454,7 +454,7 @@ def check_reductions(alt_cs, pre, aft, idx, children):
                                                                      children)
 
                             elif all([i != [] for i in children]):
-                                # We have compiled a full set of potneital
+                                # We have compiled a full set of potential
                                 # children to reduce_trees. Generate a key and check
                                 # if it exists.
                                 generate_key_and_check(pre, aft, reduce,

src/representation/individual.py

@@ -114,7 +114,7 @@ def evaluate(self):
         evaluation on either training or test distributions. Sets fitness
         value.
 
-        :return: Nothing unless multicore evaluation is being used. In that
+        :return: Nothing unless multi-core evaluation is being used. In that
         case, returns self.
         """
 

src/scripts/GE_LR_parser.py

@@ -43,18 +43,18 @@ def parse_terminals(target):
     for T in sorted(terms.keys()):
         # Iterate over all Terminals.
 
-        # Find all occurances of this terminal in the target string.
-        occurrances = []
+        # Find all occurrences of this terminal in the target string.
+        occurrences = []
         index = 0
         while index < len(target):
             index = target.find(T, index)
-            if index not in occurrances and index != -1:
-                occurrances.append(index)
+            if index not in occurrences and index != -1:
+                occurrences.append(index)
                 index += len(T)
             else:
                 break
 
-        for idx in occurrances:
+        for idx in occurrences:
             # Check each occurrence of this terminal in the target string.
 
             for NT in terms[T]:

src/utilities/algorithm/NSGA2.py

@@ -23,7 +23,7 @@ def compute_pareto_metrics(population):
 
 
 def sort_non_dominated(population):
-    """Sort the first *k* *population* into different nondomination levels
+    """Sort the first *k* *population* into different non-domination levels
     using the "Fast Nondominated Sorting Approach" proposed by Deb et al.,
     see [Deb2002]_. This algorithm has a time complexity of :math:`O(MN^2)`,
     where :math:`M` is the number of objectives and :math:`N` the number of
@@ -32,7 +32,7 @@ def sort_non_dominated(population):
     :param population: A list of individuals to select from.
 
     :returns: A list of Pareto fronts (lists), the first list includes
-              nondominated individuals.
+              non-dominated individuals.
 
     .. [Deb2002] Deb, Pratab, Agarwal, and Meyarivan, "A fast elitist
        non-dominated sorting genetic algorithm for multi-objective
@@ -100,13 +100,13 @@ def sort_non_dominated(population):
 
 def dominates(individual1, individual2):
     """
-    Returns whether or not *indvidual1* dominates *indvidual2*. An individual
+    Returns whether or not *individual1* dominates *individual2*. An individual
     dominates another if all fitness values are at least as good on all
     objectives, and strictly better than on at least one objective.
 
     :param individual1: The individual that would be dominated.
     :param individual2: The individual dominant.
-    :returns: :obj:`True` if indvidual_1 dominates indvidual_2,
+    :returns: :obj:`True` if *individual1* dominates *individual2*,
               :obj:`False` otherwise.
     """
 
@@ -235,7 +235,7 @@ def crowded_comparison_operator(self, other, pareto):
     :return: True if *self* is better than *other* and False otherwise.
     """
 
-    # Between two solutions with differing nondomination ranks, we prefer the
+    # Between two solutions with differing non-domination ranks, we prefer the
     # solution with the lower (better) rank. Otherwise, if both solutions
     # belong to the same front, then we prefer the solution that is located in
     # a lesser crowded region, i.e., with the larger crowding distance.

src/utilities/algorithm/command_line_parser.py

@@ -172,14 +172,14 @@ def __call__(self, parser, namespace, value, option_string=None):
     parser.add_argument('--codon_size',
                         dest='CODON_SIZE',
                         type=int,
-                        help='Sets the range from 0 to condon_size to be used '
+                        help='Sets the range from 0 to codon_size to be used '
                              'in genome, requires int value')
     parser.add_argument('--max_genome_length',
                         dest='MAX_GENOME_LENGTH',
                         type=int,
                         help='Sets the maximum chromosome length for the '
                              'algorithm, requires int value. The default max '
-                             'genome length is set to None, i.e. gemomes can '
+                             'genome length is set to None, i.e. genomes can '
                              'grow indefinitely. This can also be set by '
                              'specifying the max genome length to be 0.')
     parser.add_argument('--max_wraps',
@@ -234,7 +234,7 @@ def __call__(self, parser, namespace, value, option_string=None):
     parser.add_argument('--tournament_size',
                         dest='TOURNAMENT_SIZE',
                         type=int,
-                        help='Sets the number of indivs to contest tournament,'
+                        help='Sets the number of individuals to contest tournament,'
                              ' requires int.')
     parser.add_argument('--selection_proportion',
                         dest='SELECTION_PROPORTION',
@@ -348,12 +348,12 @@ def __call__(self, parser, namespace, value, option_string=None):
                         dest='MULTICORE',
                         action='store_true',
                         default=None,
-                        help='Turns on multicore evaluation.')
+                        help='Turns on multi-core evaluation.')
     parser.add_argument('--cores',
                         dest='CORES',
                         type=int,
                         help='Specify the number of cores to be used for '
-                             'multicore evaluation. Requires int.')
+                             'multi-core evaluation. Requires int.')
 
     # REPLACEMENT
     parser.add_argument('--replacement',
@@ -473,7 +473,7 @@ def __call__(self, parser, namespace, value, option_string=None):
                         dest='MULTIAGENT',
                         action='store_true',
                         default=None,                        
-                        help='This enable the multiagent mode. If this mode is'
+                        help='This enable the multi-agent mode. If this mode is'
                              ' enabled the search_loop and step parameter are' 
                              ' overridden with search_multiagent and step_multiagent'
                              ' respectively')

src/utilities/algorithm/initialise_run.py

@@ -149,15 +149,15 @@ class name matches the file name.
                     else:
                         # Just module name specified. Use default location.
 
-                        # If multiagent is specified need to change
+                        # If multi-agent is specified need to change
                         # how search and step module is called
-                        # Loop and step functions for multiagent is contained 
+                        # Loop and step functions for multi-agent is contained
                         # inside algorithm search_loop_distributed and 
                         # step_distributed respectively
 
                         if params['MULTIAGENT'] and \
                         ( op == 'SEARCH_LOOP' or op == 'STEP' ) :
-                            # Define the directory structure for the multiagent search
+                            # Define the directory structure for the multi-agent search
                             # loop and step
                             multiagent_ops = {'search_loop':'distributed_algorithm.search_loop' \
                                                 ,'step':'distributed_algorithm.step'}

src/utilities/fitness/error_metric.py

@@ -66,7 +66,7 @@ def hinge(y, yhat):
     if 0 in y_vals:
         y[y == 0] = -1
 
-    # Our definition of hinge loss cannot be used for multiclass
+    # Our definition of hinge loss cannot be used for multi-class
     assert len(y_vals) == 2
 
     # NB not np.max. maximum does element-wise max.  Also we use the
@@ -104,7 +104,7 @@ def f1_score(y, yhat):
     if -1 in y_vals:
         y[y == -1] = 0
 
-    # We binarize with a threshold, so this cannot be used for multiclass
+    # We binarize with a threshold, so this cannot be used for multi-class
     assert len(y_vals) == 2
 
     # convert real values to boolean {0, 1} with a zero threshold

src/utilities/fitness/math_functions.py

@@ -177,7 +177,7 @@ def percentile(sorted_list, p):
     in a sorted list
 
     :param sorted_list: The sorted list
-    :param p: The percetile
+    :param p: The percentile
     :return: The element corresponding to the percentile
     """
 

src/utilities/representation/check_methods.py

@@ -87,7 +87,7 @@ def check_ind_from_parser(ind, target):
     Checks the mapping of an individual generated by the GE parser against
     the specified target string to ensure the GE individual is correct.
 
-    :param ind: An instance of the representation.individaul.Individual class.
+    :param ind: An instance of the representation.individual.Individual class.
     :param target: A target string against which to match the phenotype of
     the individual.
     :return: Nothing.