July 2024
Intermediate to advanced
418 pages
10h 25m
English
Content preview from Hands-On Genetic Algorithms with Python - Second Edition
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14
Beyond Local Resources – Scaling Genetic Algorithms in the Cloud
This chapter builds on the previous one, which focused on using multiprocessing to enhance genetic algorithm performance. It restructures the genetic algorithm into a client-server model, where the client employs asynchronous I/O, and the server manages fitness function calculations. The server component is then deployed to the cloud via AWS Lambda, demonstrating a practical application of serverless architecture in optimizing genetic algorithm computations.
This chapter starts by discussing the advantages of dividing genetic algorithms into client and server components. It then progresses to implementing this client-server model while using the same One-Max benchmark problem ...
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