Optimizing Genes with a Genetic Algorithm
In the simplest terms, genetic algorithms simulate a population where each individual is a possible “solution” and let survival of the fittest do its thing.
Genetic algorithms can be a great way to tackle an optimisation problem because they can reliably find a good solution, even in a complex fitness landscape with many local optima. I’m using this technique for optimising a DNA sequence to accurately produce lots of a useful protein. Here we will be looking at the key, big picture steps of implementing a genetic algorithm rather than diving into the theory behind them, but there are plenty more detailed descriptions out there, such as here and here.
In the simplest terms genetic algorithms simulate a population where each individual is a possible “solution” and let survival of the fittest do its thing. The difficult part, as with much of data science and machine learning, is how to frame the question for the computer. We will see how to improve your solutions just by framing the question better. Continue reading...
