A Multi-Facet Survey on Memetic Computation

By Xianshun Chen, Yew-Soon Ong, Meng-Hiot Lim, Kay Chen Tan. IEEE Transactions on Evolutionary Computation, 2011

502 citations recorded by the source corpus.

Memetic computation is a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving. It covers a plethora of potentially rich meme-inspired computing methodologies, frameworks and operational algorithms including simple hybrids, adaptive hybrids and memetic automaton. In this paper, a comprehensive multi-facet survey of recent research in memetic computation is presented.

Subfields: Artificial Intelligence, Biomedical Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/cc81cd5a68ae451b65797e13d1c4442a0df1fc49

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Citations in the corpus

Multiobjective Multifactorial Optimization in Evolutionary Multitasking (2017, background): “As the working of the MO-MFEA is based on the transmission of biological and cultural building blocks from parents to offspring, the algorithm is regarded as belonging to the realm of memetic computation [26]–[28].”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “[169,170] highlight the recent advances in theory and application areas of memetic algorithm.”
Multifactorial Evolution: Toward Evolutionary Multitasking (2016, methodology): “Note that the repair is performed in the spirit of Baldwinian learning [18].”
Insights on Transfer Optimization: Because Experience is the Best Teacher (2018, background): “In the memetic computing literature [51], such computational representations of knowledge are often referred to as memes, which can either be passed from one individual to another (via imitation) [52], or even be propagated across problems [16].”