Genetic Algorithms create agents who compete to find the best solution to a problem using evolution.
Agents are initially created either using the best chromosomes from a previous run or a random mixture of chromosomes. After each round we evaluate each agent to see how well he has done. We then mix up the genes by switching among our best agents and or adding in some mutations. We repeat this for a set number of rounds or until some goal is reached.
If only gene switching is done and no mutations are entered into the population the agents will often only find local maximums not global maximums. If the number of agents is too small a solution may not be found.
Simple Genetic Program - you have predators, prey and food for the prey. When either a predator or prey reaches reproduction energy 2 of 3 genes are passed directly to the child, one is random.
GeneticLife.java
See Also:
Bioinformatics Blog: Genetic Algorithms: A Quick Tutorial
Racing with Evolutionary Algorithms
3 responses so far ↓
1 Nosophorus // Jun 13, 2007 at 6:17 am
Hi!
Thank you for the link to my blog!
I hope you had enjoyed the post upon the walking robots.
See Ya!
Marcelo
2 Anonymous // Jun 28, 2007 at 7:24 am
Hi! I am currently writing a (free and open) e-book called “Global Optimization - Theory and Application”. It is about genetic algorithms, evolutionary algorithms, evolution strategy, leaning classifier systems, simulated annealing, and so on. I hope that I can make this topic more interesting for students and fellow researchers and help people to get started with it. Of course, my book is still very incomplete and probably has also errors, but I try to improve it very much. You can find it at http://www.it-weise.de/projects/book.pdf, I will update it regularly. Constructive criticism is always welcom, as well as new suggestions on what to include in the book.
Kind regards,
Thomas.
3 herself // Jun 28, 2007 at 1:51 pm
Thank you for letting me know. I’m sure there will be people here interested in your book.
Good luck with it.
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