Editing Evolutionary algorithm (section)

===Monte-Carlo methods===
Both method classes have in common that their individual search steps are determined by chance. The main difference, however, is that EAs, like many other metaheuristics, learn from past search steps and incorporate this experience into the execution of the next search steps in a method-specific form. With EAs, this is done firstly through the fitness-based selection operators for partner choice and the formation of the next generation. And secondly, in the type of search steps: In EA, they start from a current solution and change it or they mix the information of two solutions. In contrast, when dicing out new solutions in [[Monte Carlo method|Monte-Carlo methods]], there is usually no connection to existing solutions.<ref>{{Cite book |last=Schwefel |first=Hans-Paul |url=https://www.researchgate.net/publication/220690578 |title=Evolution and Optimum Seeking |date=1995 |publisher=Wiley |isbn=978-0-471-57148-3 |series=Sixth-generation computer technology series |location=New York |pages=109}}</ref><ref>{{Cite book |url=https://www.worldcat.org/title/ocm44807816 |title=Evolutionary Computation 1 |date=2000 |publisher=Institute of Physics Publishing |isbn=978-0-7503-0664-5 |editor-last=Fogel |editor-first=David B. |location=Bristol ; Philadelphia |pages=xxx and xxxvii (Glossary) |oclc=ocm44807816 |editor-last2=Bäck |editor-first2=Thomas |editor-last3=Michalewicz |editor-first3=Zbigniew}}</ref>

If, on the other hand, the search space of a task is such that there is nothing to learn, Monte-Carlo methods are an appropriate tool, as they do not contain any algorithmic overhead that attempts to draw suitable conclusions from the previous search. An example of such tasks is the proverbial ''search for a needle in a haystack'', e.g. in the form of a flat (hyper)plane with a single narrow peak.