In many cases, optimization problems have local maxima and minima points; these represent solutions that are better than those around them, but not the best overall.
The following image illustrates the differences between global and local maxima and minima points:
The global and local maxima and minima of a function
Source: https://commons.wikimedia.org/wiki/File:Computational.science.Genetic.algorithm.Crossover.One.Point.svg.
Image by KSmrq. Licensed under Creative Commons CC BY-SA 3.0: https://creativecommons.org/licenses/by-sa/3.0/
Most traditional search and optimization algorithms, and particularly those that are gradient-based, ...
