To preface this article, I want to say that I am absolutely biased. Genetic algorithms are the only remnants I have of my past life, as a biology major from Cornell. The algorithms are not only elegant in their design, mimicking the real-life process of natural selection and evolution, but they’re also surprisingly intuitive… if you’re a biology major.
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However, the Machine Learning literature has been surprisingly lacking in papers related to evolutionary algorithms and genetic optimization. In all honesty, outside of my automated trading platform NexusTrade, which uses genetic algorithms for strategy optimization, I haven’t seen a real-life application of genetic algorithms since I first learned about them 4 years ago. This begs the questions… is genetic optimization dead?
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Brief Recap: What is a Genetic Algorithm?
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Genetic algorithms (GAs) are a biologically-inspired AI search heuristic capable of finding good solutions in a very large search space. They work by taking notes from natural selection, in which a population of solutions mixes their genes through crossover to produce new solutions that, while bearing resemblance to the previous generation, often manifest as slightly altered variants. Like in the natural world, mutations further introduce variations within the population, ensuring a diverse set of solutions that help in exploring the search space effectively.
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Unlike some other optimization techniques such as gradient descent, genetic algorithms can optimize any arbitrary function, including functions that are non-differentiable. What makes them unique is, instead of generating one solution, they often generate populations of solutions each with their own strengths and weaknesses. Theoretically, this makes it easy to pick between different solutions depending on what you’re willing to trade-off.
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