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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6
Optimizing Continuous Functions
This chapter describes how continuous search-space optimization problems can be solved by genetic algorithms. We will start by describing the chromosomes and genetic operators commonly used for genetic algorithms with real number-based populations and go over the tools offered by the Distributed Evolutionary Algorithms in Python (DEAP) framework for this domain. We will then cover several hands-on examples of continuous function optimization problems and their Python-based solutions using the DEAP framework. These include the optimization of the Eggholder function, Himmelblau’s function, as well as the constrained optimization of Simionescu’s function. Along the way, we will learn about finding multiple solutions ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.