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Using genetic algorithms on AWS for optimization problems

 Using genetic algorithms on AWS for optimization problems

Machine learning (ML)-based solutions are capable of solving complex problems, from voice recognition to finding and identifying faces in video clips or photographs. Usually, these solutions use large amounts of training data, which results in a model that processes input data and produces numeric output that can be interpreted as a word, face, or classification category. For many types of problems, this approach works very well.

But what if you have a problem that doesn’t have training data available, or doesn’t fit within the concept of a classification or regression? For example, what if you need to find an optimal ordering for a given set of worker tasks with a given set of conditions and constraints? How do you solve that, especially if the number of tasks is very large?

This post describes genetic algorithms (GAs) and demonstrates how to use them on AWS. GAs are unsupervised ML algorithms used to solve general types of optimization problems, including:

  • Optimal data orderings – Examples include creating work schedules, determining the best order to perform a set of tasks, or finding an optimal path through an environment
  • Optimal data subsets – Examples include finding the best subset of products to include in a shipment, or determining which financial instruments to include in a portfolio
  • Optimal data combinations – Examples include finding an optimal strategy for a task that is composed of many components, where each component is a choice of one of many options

For many optimization problems, the number of potential solutions (good and bad) is very large, so GAs are often considered a type of search algorithm, where the goal is to efficiently search through a huge solution space. GAs are especially advantageous when the fitness landscape is complex and non-convex, so that classical optimization methods such as gradient descent are an ineffective means to find a global solution. Finally, GAs are often referred to as heuristic search algorithms because they don’t guarantee finding the absolute best solution, but they do have a high probability of finding a sufficiently good solution to the problem in a short amount of time.

GAs use concepts from evolution such as survival of the fittest, genetic crossover, and genetic mutation to solve problems. Rather than trying to create a single solution, those evolutionary concepts are applied to a population of different problem solutions, each of which is initially random. The population goes through a number of generations, literally evolving solutions through mechanisms like reproduction (crossover) and mutation. After a number of generations of evolution, the best solution found across all the generations is chosen as the final problem solution.

As a prerequisite to using a GA, you must


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