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Summary of the Process
1.Begin with a randomly generated population of input combinations (a.k.a. individuals)  
2.Calculate the fitness of each individual, where fitness is a measure of how well the strategy performs using a set of inputs  
3.Retain the fittest members, discarding the least fit members  
4.Generate a new population of individuals from the remaining members of the old population by applying the crossover, mutation and complement operations  
5.Calculate the fitness of these new individuals retaining the fittest, discarding the least fit.  
6.Repeat until convergence is reached  

Further Reading
This is only a brief introduction to genetic algorithms. We encourage you to read more about them to fully understand their functions, capabilities, and limitations. Many excellent guides can be found on the Internet:

·The Hitch-Hiker's Guide to Evolutionary Computation  
·A Brief Primer on Genetic Algorithms  
·Local Search Algorithms  
·Genetic Algorithms as a Computational Tool for Design  
·Using Multiple Chromosomes To Solve a Simple Mixed Integer Problem