Help understanding genetic algorithms please

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The discussion revolves around the challenges faced in understanding the fitness function within genetic algorithms. The original poster seeks assistance after studying the evolution of the fitness function but struggling to grasp its application. A response highlights the importance of specificity in the inquiry, suggesting that sharing details about the programming language or specific issues encountered could lead to more targeted help. The key components of genetic algorithms are outlined: generating new solution vectors through mating, scoring these vectors using the fitness function, and selecting the top-performing solutions for further iterations. The process continues until a stopping criterion is met, such as convergence of scores or a reduction in the solution pool.
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HELLO, i have started working on Genetic Algorithm , i have studied evloution of fitness function, but i really could not get it. please help me in this regard
wasi
HELLO, i have started working on Genetic Algorithm , i have studied evloution of fitness function, but i really could not get it. please help me in this regard
wasi
 
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I am not familiar with this subject, but I think it would be helpful if you were more specific about what you are doing. There are several books on the subject. Are you reading a particular book on programming genetic algorithms? Do you have a particular program in some language that is not working? What is going wrong?
 
Genetic algorithms have three parts:
- generate a new set of possible solution vectors from the existing set mating two or more solution vectors to make a new one
- using the fitness function to score each solution vector
- selecting those solution vectors with scores over a certain threshold value or by selecting the top N solutions and discarding the rest

And then repeat the process until some criteria like all scores are about the same or the pool of solutions gets too small.
 

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