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"AttributeError: fileno" when attemping to import from pyevolve

- by Corey Sunwold

I just installed Pyevolve using easy_install and I am getting errors trying to run my first program. I first tried copy and pasting the source code of the first example but this is what I receive when I attempt to run it: Traceback (most recent call last): File "/home/corey/CTest/first_intro.py", line 3, in from pyevolve import G1DList File "/usr/lib/python2.6/site-packages/Pyevolve-0.5-py2.6.egg/pyevolve/init.py", line 15, in File "/usr/lib/python2.6/site-packages/Pyevolve-0.5-py2.6.egg/pyevolve/Consts.py", line 240, in import Selectors File "/usr/lib/python2.6/site-packages/Pyevolve-0.5-py2.6.egg/pyevolve/Selectors.py", line 12, in File "/usr/lib/python2.6/site-packages/Pyevolve-0.5-py2.6.egg/pyevolve/GPopulation.py", line 11, in File "/usr/lib/python2.6/site-packages/Pyevolve-0.5-py2.6.egg/pyevolve/FunctionSlot.py", line 14, in File "/usr/lib/python2.6/site-packages/Pyevolve-0.5-py2.6.egg/pyevolve/Util.py", line 20, in AttributeError: fileno I am running python 2.6 on Fedora 11 X86_64.

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ImportError: No module named _sqlite3

- by Chris R.

I'm writing for the Google App Engine and my local tests are getting the following error: --> --> --> Traceback (most recent call last): File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 3185, in _HandleRequest self._Dispatch(dispatcher, self.rfile, outfile, env_dict) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 3128, in _Dispatch base_env_dict=env_dict) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 515, in Dispatch base_env_dict=base_env_dict) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 2387, in Dispatch self._module_dict) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 2297, in ExecuteCGI reset_modules = exec_script(handler_path, cgi_path, hook) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 2193, in ExecuteOrImportScript exec module_code in script_module.__dict__ File "C:\Users\Chris Reade\Documents\SI 182\Final\geneticsalesman\Final.py", line 7, in <module> from pyevolve import DBAdapters File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1272, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1922, in load_module return self.FindAndLoadModule(submodule, fullname, search_path) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1272, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1824, in FindAndLoadModule description) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1272, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1775, in LoadModuleRestricted description) File "C:\Users\Chris Reade\Documents\SI 182\Final\geneticsalesman\pyevolve\DBAdapters.py", line 21, in <module> import sqlite3 File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1272, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1922, in load_module return self.FindAndLoadModule(submodule, fullname, search_path) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1272, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1824, in FindAndLoadModule description) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1272, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1775, in LoadModuleRestricted description) File "C:\Python26\lib\sqlite3\__init__.py", line 24, in <module> from dbapi2 import * File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1272, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1922, in load_module return self.FindAndLoadModule(submodule, fullname, search_path) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1272, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1824, in FindAndLoadModule description) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1272, in Decorate return func(self, *args, **kwargs) File "C:\Program Files\Google\google_appengine\google\appengine\tools\dev_appserver.py", line 1775, in LoadModuleRestricted description) File "C:\Python26\lib\sqlite3\dbapi2.py", line 27, in <module> from _sqlite3 import * ImportError: No module named _sqlite3 My python direction has a lib file for sqlite3 but I can't tell why it can't find it. Any help would be greatly appreciated.

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Sparse parameter selection using Genetic Algorithm

- by bgbg

Hello, I'm facing a parameter selection problem, which I would like to solve using Genetic Algorithm (GA). I'm supposed to select not more than 4 parameters out of 3000 possible ones. Using the binary chromosome representation seems like a natural choice. The evaluation function punishes too many "selected" attributes and if the number of attributes is acceptable, it then evaluates the selection. The problem is that in these sparse conditions the GA can hardly improve the population. Neither the average fitness cost, nor the fitness of the "worst" individual improves over the generations. All I see is slight (even tiny) improvement in the score of the best individual, which, I suppose, is a result of random sampling. Encoding the problem using indices of the parameters doesn't work either. This is most probably, due to the fact that the chromosomes are directional, while the selection problem isn't (i.e. chromosomes [1, 2, 3, 4]; [4, 3, 2, 1]; [3, 2, 4, 1] etc. are identical) What problem representation would you suggest? P.S If this matters, I use PyEvolve.

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