Grammatical evolution

Grammatical evolution is a computational technique that involves evolving binary strings to select production rules in a grammar definition, which can be used to generate any kind of programs. It is a related concept to symbolic regression and is used to define a problem with a grammar and a fitness function.From: Real Life Applications of Soft Computing [2019], A survey on evolutionary machine learning [2019], Soft Computing in Wireless Sensor Networks [2018]

Soft Computing Technique for Intrusion Detection System in Mobile Ad Hoc Networks

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Published in Huynh Thi Thanh Binh, Nilanjan Dey, Soft Computing in Wireless Sensor Networks, 2018

V. Bapuji, B. Manjula, D. Srinivas Reddy

In grammatical evolution, a problem is defined with a grammar and a fitness function. The following GE parameters are used for experiments (as shown in Table 5.2) to detect ad hoc flooding and route disruption attacks of MANETs.

A survey on evolutionary machine learning

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Published in Journal of the Royal Society of New Zealand, 2019

Harith Al-Sahaf, Ying Bi, Qi Chen, Andrew Lensen, Yi Mei, Yanan Sun, Binh Tran, Bing Xue, Mengjie Zhang

Some other EC techniques are also used for symbolic regression. However, most of them are in their infancy. LCS was recently used for symbolic regression for the first time (Naqvi and Browne 2016). AIS (Johnson 2003) was applied to solve symbolic regression tasks. Grammatical evolution (O’Neill and Ryan 2001), which evolves binary strings to select production rules in a grammar definition to generate any kind of programmes, can also be used for symbolic regression.

Grammatical evolution

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Soft Computing Technique for Intrusion Detection System in Mobile Ad Hoc Networks

A survey on evolutionary machine learning