Part 4: Enhancing Performance with Concurrency and Cloud Strategies

This part focuses on enhancing the performance of genetic algorithms through advanced programming techniques, specifically concurrency and cloud computing. The first chapter introduces concurrency, especially multiprocessing, as a tool to improve genetic algorithm efficiency. The concept is illustrated by applying various multiprocessing methods to a CPU-intensive version of the One-Max problem, demonstrating significant performance gains. Building on this, the next chapter shifts to a client-server model, partitioning the genetic algorithm between asynchronous client operations and server-based fitness function computations. This model is then practically implemented using Flask for the server and Python’s asyncio for the client, culminating in deployment to the cloud via AWS Lambda.

This part contains the following chapters:

• Chapter 13, Accelerating Genetic Algorithms: The Power of Concurrency
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