Commit 2e82d3fa by Imanol Pérez Committed by GitHub

Update README.md

parent 3f62dbcb
......@@ -6,11 +6,11 @@ This code tries to show how to use genetic algorithms to create a simple trading
Genetic algorithms simulate the evolution of a species. It starts with a random population of organisms, which represent algorithms. Then, after many generations the strongest algorithms survive. In general, genetic algorithms possess the following structure:
1) Generate a random population of organisms
2) Select the top performers, acording to a certain criteria (a fitness function is usually required here).
3) Perform a crossover of the outperformers to produce new children.
4) Apply a random mutation to some of the children. This way, we end up with a new generation of organisms.
5) Repeat step 2), until the desired number of generations is achieved.
1. Generate a random population of organisms
2. Select the top performers, acording to a certain criteria (a fitness function is usually required here).
3. Perform a crossover of the outperformers to produce new children.
4. Apply a random mutation to some of the children. This way, we end up with a new generation of organisms.
5. Repeat step 2), until the desired number of generations is achieved.
## The code
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