Don't warn if a type already exists.

deap/creator.py

@@ -133,13 +133,6 @@ def __init__(self):
        :doc:`/examples/ga_onemax_numpy` example.
 
     """
-
-    if name in globals():
-        warnings.warn("A class named '{0}' has already been created and it "
-                      "will be overwritten. Consider deleting previous "
-                      "creation of that class or rename it.".format(name),
-                      RuntimeWarning)
-
     dict_inst = {}
     dict_cls = {}
     for obj_name, obj in kargs.items():

deap/tools/_hypervolume/_hv.c

@@ -353,7 +353,7 @@ avl_unlink_node(avl_tree_t *avltree, avl_node_t *avlnode) {
 /*
  * avl_rebalance:
  * Rebalances the tree if one side becomes too heavy.  This function
- * assumes that both subtrees are AVL-trees with consistant data.  The
+ * assumes that both subtrees are AVL-trees with consistent data.  The
  * function has the additional side effect of recalculating the count of
  * the tree at this node.  It should be noted that at the return of this
  * function, if a rebalance takes place, the top of this subtree is no
@@ -1109,7 +1109,7 @@ test_dom(dlnode_t *list, dlnode_t *pp, int dim, int *order)
        at most, up to dim dimensions, and so if p0 dominates pp in
        the first y dimensions (y < dim), the point that dominates
        p0 also dominates pp in the first y dimensions or more, and
-       this informations is already stored in r_b), so p0 is
+       this information is already stored in r_b), so p0 is
        skipped.  */
     while (p0 != pp) {
         if (p0->ignore < dim) {
@@ -1225,7 +1225,7 @@ determine_ndomf(dlnode_t *list, int dim, int c, int *order, int *count)
 
 
 /*
-  This funtion implements the iterative version of MDP heuristic described in
+  This function implements the iterative version of MDP heuristic described in
   L. While, L. Bradstreet, L. Barone, and P. Hingston, "Heuristics for optimising
   the calculation of hypervolume for multi-objective optimisation problems", in
   Congress on Evolutionary Computation, B. McKay, Ed. IEEE, 2005, pp. 2225-2232
@@ -1235,10 +1235,10 @@ determine_ndomf(dlnode_t *list, int dim, int c, int *order, int *count)
    This algorithm tries to maximize the number of dominated points
    dominated in more dimensions.  For example, for a problem with d
    dimensions, an order with 20 points dominated from dimension 0 to
-   dimension d-1 is prefered to an order of objectives in which the
+   dimension d-1 is preferred to an order of objectives in which the
    number of points dominated from dimension 0 to d-1 is 10. An order
    with the same number of points dominated up to dimension d-1 as a
-   second order is prefered if it has more points dominated up to
+   second order is preferred if it has more points dominated up to
    dimension d-2 than the second order.  */
 static int define_order(dlnode_t *list, int dim, int c, int *order)
 {

doc/contributing.rst

@@ -71,7 +71,7 @@ Same as PEP8.
 Imports
 +++++++
 
-First imports in a file are the standard library module, then come the imports of eap module, and finally the custom module for a problem. Each block of imports should be separated by a new line.
+First imports in a file are the standard library module, then come the imports of deap module, and finally the custom module for a problem. Each block of imports should be separated by a new line.
 
 ::
 

examples/ga/xkcd.py

@@ -59,7 +59,7 @@ def evalXKCD(individual, target_price):
     return abs(price - target_price), max(times)
 
 def cxCounter(ind1, ind2, indpb):
-    """Swaps the number of perticular items between two individuals"""
+    """Swaps the number of particular items between two individuals"""
     for key in ITEMS.keys():
         if random.random() < indpb:
             ind1[key], ind2[key] = ind2[key], ind1[key]