Editing Computational intelligence (section)

=== Evolutionary computation ===
Evolutionary computation can be seen as a family of methods and algorithms for [[global optimization]], which are usually based on a [[Population model (evolutionary algorithm)|population]] of candidate solutions. They are inspired by [[biological evolution]] and are often summarized as [[evolutionary algorithm]]s.<ref>{{Cite book |last=De Jong |first=Kenneth A. |url=https://ieeexplore.ieee.org/book/6267245 |title=Evolutionary Computation: A Unified Approach. |publisher=MIT Press |year=2006 |isbn=978-0-262-52960-0 |location=Cambridge, MA |language=en}}</ref> These include the [[genetic algorithm]]s, [[evolution strategy]], [[genetic programming]] and many others.<ref>{{Cite book |last1=Eiben |first1=A.E. |url=https://link.springer.com/10.1007/978-3-662-44874-8 |title=Introduction to Evolutionary Computing |last2=Smith |first2=J.E. |date=2015 |publisher=Springer |isbn=978-3-662-44873-1 |series=Natural Computing Series |location=Berlin, Heidelberg |pages=99–116 |language=en |chapter=Popular Evolutionary Algorithm Variants |doi=10.1007/978-3-662-44874-8}}</ref> They are considered as problem solvers for tasks not solvable by traditional mathematical methods<ref>{{Cite book |last=De Jong |first=Kenneth A. |url=https://ieeexplore.ieee.org/book/6267245 |title=Evolutionary Computation: A Unified Approach |publisher=MIT Press |year=2006 |isbn=978-0-262-52960-0 |location=Cambridge, MA |pages=71–114 |language=en |chapter=Evolutionary Algorithms as Problem Solvers}}</ref> and are frequently used for [[Optimization (computer science)|optimization]] including [[multi-objective optimization]].<ref>{{Cite book |url=http://link.springer.com/10.1007/978-3-540-88908-3 |title=Multiobjective Optimization: Interactive and Evolutionary Approaches |date=2008 |publisher=Springer Berlin Heidelberg |isbn=978-3-540-88907-6 |editor-last=Branke |editor-first=Jürgen |series=Lecture Notes in Computer Science |volume=5252 |location=Berlin, Heidelberg |language=en |doi=10.1007/978-3-540-88908-3 |editor-last2=Deb |editor-first2=Kalyanmoy |editor-last3=Miettinen |editor-first3=Kaisa |editor-last4=Słowiński |editor-first4=Roman}}</ref> Since they work with a population of candidate solutions that are processed in parallel during an iteration, they can easily be distributed to different computer nodes of a cluster.<ref>{{Cite book |last=Cantú-Paz |first=Erick |title=Efficient and Accurate Parallel Genetic Algorithms |date=2001 |publisher=Springer US |isbn=978-1-4613-6964-6 |series=Genetic Algorithms and Evolutionary Computation |volume=1 |location=New York, NY |doi=10.1007/978-1-4615-4369-5}}</ref> As often more than one offspring is generated per pairing, the evaluations of these offspring, which are usually the most time-consuming part of the optimization process, can also be performed in parallel.<ref name=":72">{{Citation |last1=Khalloof |first1=Hatem |title=A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based Metaheuristics |date=2020-11-02 |work=Proceedings of the 12th International Conference on Management of Digital EcoSystems (MEDES'20) |pages=124–131 |url=https://dl.acm.org/doi/10.1145/3415958.3433041 |location=New York, NY |publisher=ACM |language=en |doi=10.1145/3415958.3433041 |isbn=978-1-4503-8115-4 |s2cid=227179748 |last2=Mohammad |first2=Mohammad |last3=Shahoud |first3=Shadi |last4=Duepmeier |first4=Clemens |last5=Hagenmeyer |first5=Veit|url-access=subscription }}</ref>

In the course of optimization, the population learns about the structure of the search space and stores this information in the chromosomes of the solution candidates. After a run, this knowledge can be reused for similar tasks by adapting some of the “old” chromosomes and using them to seed a new population.<ref>{{Cite journal |last1=Jakob |first1=Wilfried |last2=Strack |first2=Sylvia |last3=Quinte |first3=Alexander |last4=Bengel |first4=Günther |last5=Stucky |first5=Karl-Uwe |last6=Süß |first6=Wolfgang |date=2013-04-22 |title=Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing |journal=Algorithms |language=en |volume=6 |issue=2 |pages=245–277 |doi=10.3390/a6020245 |issn=1999-4893 |doi-access=free}}</ref><ref>{{Cite journal |last1=Friedrich |first1=Tobias |last2=Wagner |first2=Markus |date=August 2015 |title=Seeding the initial population of multi-objective evolutionary algorithms: A computational study |journal=Applied Soft Computing |language=en |volume=33 |pages=223–230 |doi=10.1016/j.asoc.2015.04.043|arxiv=1412.0307 }}</ref>