Genetic Algorithms:“Non-Smooth” Discrete Optimization
In Lesson 7, we described an algorithm (called simulated annealing) that solves “almost smooth” discrete optimization problems, i.e., problems in which a “small” change in the point x leads to a small change in the value of the objective function J(x). In this lesson, we consider “non-smooth” discrete optimization problems. For such problems, a different class of algorithms has been developed: genetic algorithms that simulate evolution in nature.
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