Introduction
Explore how population-based heuristics mimic evolution to solve optimization problems when exact algorithms are not feasible. Understand the role of genetic algorithms and particle swarm optimization in navigating large solution spaces and learn practical approaches for applying these metaheuristic techniques in Python.
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The theory of evolution states that the different forms of life we see today are the result of millions of years of evolution, mutations, and adaptation. The specimens that developed mutations that gave them an advantage over the others prevailed, and that mutation was transmitted to the next generation. That’s how evolution explains the existence of complex life forms like ourselves.
We’re not going to start a debate about the theory of evolution, but this is an idea that’s used in the world of optimization. Evolution, as it’s described in Charles Darwin’s theory, is not a carefully designed process that should converge to the organisms we see today. It has no requirements to ensure convergence. It’s just a process that goes on for millions of years and produces a result as a consequence.
In optimization, we can do the same. Sometimes the functions are very complicated or even unknown, so we can’t assure any requirements or behavior. We can’t assure convergence. Then we just start trying ...