Month: September 2009

News, Science

On the irreversibility of evolution

evo_comic

Today I’ve read about an important work done by a team of evolutionary biologists of the University of Oregon, which reveals an important result about the evolutionary irreversibility. The concept of irreversibility states that the future results of evolution at any point in time must depend on the present state and by the past, showing the determinism of evolution; on the other hand, the evolution reversibility dictates that the natural selection can produce the same forms in any given environment, independent of history.

This question about the irreversibility of evolution has remained unsolved because of the fact that we rarely know what features the ancestors had and what the mechanisms was used to evolve into the actual organisms, but the team of Joe Thornton has solved those issues by studying the problem at the molecular level, resurrecting ancestral proteins (GR1) as they existed long ago and using manipulation to study evolutionary process in two directions: forward and reverse.

The results of the work done by the research team was:

Our observations suggest that history and contingency during glucocorticoid receptor evolution strongly limited the pathways that could be deterministically followed under selection.

(…)

Selection is an extraordinarily powerful evolutionary force; nevertheless, our observations suggest that, because of the complexity of glucocorticoid receptor architecture, low-probability permissive substitutions were required to open some mutational trajectories to exploration under selection, whereas restrictive substitutions closed other potential paths. Under selection, some kind of adaptation will always occur, but the specific adaptive forms that are realized depend on the historical trajectory that precedes them. The conditions that once facilitated evolution of the glucocorticoid receptor’s ancestors were destroyed during the realization of its present form. The past is difficult to recover because it was built on the foundation of its own history, one irrevocably different from that of the present and its many possible futures.

So my friend, that’s the way nature evolve, possible never looking back. But this is a great new step for future works and research on the irreversibility of evolution.

References

[1] http://www.nature.com/nature/journal/v461/n7263/abs/nature08249.html
[2] http://www.uoregon.edu/~joet/PDF/bridgham-thornton-nature2009.pdf
[3] http://sciencenow.sciencemag.org/cgi/content/full/2009/923/1

by Christian S. Perone
Genetic Algorithms, Pyevolve, Python

n-queens problem using Pyevolve

Last night I’ve read a post on Reddit written by Matthew Rollings showing a code in Python to solve Eight Queens puzzle using EA. So I decided to implement it in Python again but this time using Pyevolve, here is the code:

from pyevolve import *
from random import shuffle

BOARD_SIZE = 64

def queens_eval(genome):
   collisions = 0
   for i in xrange(0, BOARD_SIZE):
      if i not in genome: return 0
   for i in xrange(0, BOARD_SIZE):
      col = False
      for j in xrange(0, BOARD_SIZE):
         if (i != j) and (abs(i-j) == abs(genome[j]-genome[i])):
            col = True
      if col == True: collisions +=1
   return BOARD_SIZE-collisions

def queens_init(genome, **args):
   genome.genomeList = range(0, BOARD_SIZE)
   shuffle(genome.genomeList)

def run_main():
   genome = G1DList.G1DList(BOARD_SIZE)
   genome.setParams(bestrawscore=BOARD_SIZE, rounddecimal=2)
   genome.initializator.set(queens_init)
   genome.mutator.set(Mutators.G1DListMutatorSwap)
   genome.crossover.set(Crossovers.G1DListCrossoverCutCrossfill)
   genome.evaluator.set(queens_eval)

   ga = GSimpleGA.GSimpleGA(genome)
   ga.terminationCriteria.set(GSimpleGA.RawScoreCriteria)
   ga.setMinimax(Consts.minimaxType["maximize"])

   ga.setPopulationSize(100)
   ga.setGenerations(5000)
   ga.setMutationRate(0.02)
   ga.setCrossoverRate(1.0)

   # This DBAdapter is to create graphs later, it'll store statistics in
   # a SQLite db file
   sqlite_adapter = DBAdapters.DBSQLite(identify="queens")
   ga.setDBAdapter(sqlite_adapter)

   ga.evolve(freq_stats=10)

   best = ga.bestIndividual()
   print best
   print "\nBest individual score: %.2f\n" % (best.score,)

if __name__ == "__main__":
   run_main()

It tooks 49 generations to solve a 64×64 (4.096 chess squares) chessboard, here is the output:

Gen. 0 (0.00%): Max/Min/Avg Fitness(Raw) [20.83(27.00)/13.63(7.00)/17.36(17.36)]
Gen. 10 (0.20%): Max/Min/Avg Fitness(Raw) [55.10(50.00)/39.35(43.00)/45.92(45.92)]
Gen. 20 (0.40%): Max/Min/Avg Fitness(Raw) [52.51(55.00)/28.37(24.00)/43.76(43.76)]
Gen. 30 (0.60%): Max/Min/Avg Fitness(Raw) [67.45(62.00)/51.92(54.00)/56.21(56.21)]
Gen. 40 (0.80%): Max/Min/Avg Fitness(Raw) [65.50(62.00)/19.89(31.00)/54.58(54.58)]

        Evolution stopped by Termination Criteria function !

Gen. 49 (0.98%): Max/Min/Avg Fitness(Raw) [69.67(64.00)/54.03(56.00)/58.06(58.06)]
Total time elapsed: 39.141 seconds.

And here is the plots generated by the Graph Plot Tool of Pyevolve:

fig1

fig3

fig5

fig8

by Christian S. Perone
Python, Time Waste

Word is smart, but OpenOffice is wise

UPDATE 19/09: it seems that some people had misunderstood the post title, so here is a clarification: I’m not comparing Word with OpenOffice or something like that, the title refers to the design choices of OpenOffice in using Python 2.6 as an option for scripting language, it’s a humorous title and should not be considered in literal sense.

This is a very simple, but powerful, Python script to call Google Sets API (in fact it’s not an API call – since Google doesn’t have an official API for Sets service – but an interesting and well done scraper using BeautifulSoup) inside the OpenOffice 3.1.1 Writer… anyway, you can check the video to understand what it really does:

And here is the very complex source-code:

from xgoogle.googlesets import GoogleSets

def growMyLines():
   """ Calls Google Set unofficial API (xgoogle) """
   doc = XSCRIPTCONTEXT.getDocument()
   controller = doc.getCurrentController()
   selection = controller.getSelection()
   count = selection.getCount();
   text_range = selection.getByIndex(0);
   lines_list = text_range.getString().split("\n");
   gset = GoogleSets(lines_list)
   gset_results = gset.get_results()
   results_concat = "\n".join(gset_results)
   text_range.setString(results_concat);

g_exportedScripts = growMyLines,

You need to put the “xgoogle” module inside the “OpenOffice.org 3\Basis\program\python-core-2.6.1\lib” path, and the above script inside “OpenOffice.org 3\Basis\share\Scripts\python”.

I hope you enjoyed =) with new Python 2.6 core in OpenOffice 3, they have increased the productivity potential at the limit.

by Christian S. Perone

I'm starting a new course "Machine Learning: Foundations and Engineering" for 2024.

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