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I am trying to understand how genetic algorithms work. As with everything I learn by attempting to write something on my on;however, my knowledge is very limited and I am not sure if I am doing this right.

The purpose of this algorithm is to see how long it will take half of a herd to be infected by a disease if half of that population are already infected. It is just an example I came up with in my head so I am not sure if this would even be a viable example.

Some feedback on how I can improve my knowledge would be nice.

Here is the code:

import random

def disease():
    herd = []
    generations = 0
    pos = 0
    for x in range(100):
        herd.append(random.choice('01'))
    print herd
    same = all(x == herd[0] for x in herd)
    while same == False:
        same = all(x == herd[0] for x in herd)
        for animal in herd:
            try:
                if pos != 0:
                    after = herd[pos+1]
                    before = herd[pos-1]
                    if after == before and after == '1' and before == '1' and animal == '0':
                        print "infection at", pos
                        herd[pos] = '1'
            #print herd
                pos += 1
            except IndexError:
                pass
        pos = 0
        generations += 1
        random.shuffle(herd)
        #print herd
    print "Took",generations,"generations to infect all members of herd."
if __name__ == "__main__":
    disease()
iTech
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Max00355
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  • Indeed, what *is* wrong with your algorithm? You tell us. Are you getting any errors? Does it produce an unexpected output? – Volatility Feb 09 '13 at 05:01
  • No errors, I am referring to the logic of my code. I am not sure if it is 100% accurate. It is hard to debug logic. – Max00355 Feb 09 '13 at 05:04
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    This might be better suited for http://codereview.stackexchange.com/ – tacaswell Feb 09 '13 at 05:10
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    Also, this isn't a genetic algorithm, you are just iterating over the herd to spread the infection. genetic algorithms are a way to minimize of in very non-smooth phase spaces. You have some fitness function (which defines how how good your current location in phase space is) and the you select and 'breed' those locations in successive generations. In short, genetic algorithms are a type of optimization algorithm and this is not. – tacaswell Feb 09 '13 at 05:18
  • This is the type of feedback I was looking for. I am very ignorant when it comes to this topic but it is very interesting to me. I appreciate the feedback. – Max00355 Feb 09 '13 at 05:21

1 Answers1

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Your code does not implement GeneticAlgorithm. I suggest you start first by an open source library to understand how it is work before you implement your own (if needed)

To have a Genetic Algorithm you will need the following:

1- Objective function that you are trying to minimize

2- Chromosome representation (e.g. real value) that model the decision variables in your objective function. Your target is to find the best chromosome that minimize the objective function

3- Initial population of chromosomes to start your search with (can be random)

4- Genetic operators i.e. selection, crossover and mutation that you apply to the current population to get to the next generation

5- Iterate until you reach a stopping criterion e.g. maximum number of generation or desired fitness value

This is just a brief description to what Genetic Algorithm implementation should have.

iTech
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  • Thank you for the feedback, I am aware of the flaws within my code which is why I posted this! Can you suggest any libraries I can look into? – Max00355 Feb 09 '13 at 06:37
  • I know few Java libraries see this question http://stackoverflow.com/questions/3300423/which-java-library-libraries-for-genetic-algorithms – iTech Feb 09 '13 at 06:40