# Thanks to Victor Venema of the University of Bonn for the image depicting an evolutionary search algorithm
Current-day so-called Evolutionary Algorithms (EA) are more about Mendelian selective breeding than about Darwinian evolution.
Nature has no preconceived objectives. Evolution is open-ended, and is more about adaptation to the ever-evolving environment than about survival of the fittest. Although the “fittest” can be defined as those that have adapted in the best to the changing environment.
Using Evolutionary Algorithms such as Genetic Algorithms to find optimal solutions to decision-making is just directing the algorithm to a static goal. We cannot model the richness, complexity and dynamism of Nature’s meandering adaptive walks.
The road of evolution knows not its final destination. It twists and winds its way; the direction of the next step is the net sum of the interaction (actions and reactions) between all participants at that point in time plus the lingering effects of points further backwards in time.
Thus Evolution is a Markov chain with coevolution and feedback loops at the core of the evolutionary process.