We introduce a form of neutral Horizontal Gene Transfer (HGT) to Evolving Graphs by Graph Programming (EGGP). We introduce the μ × λ evolutionary algorithm, where μ parents each produce λ children who compete with only their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from 14 symbolic regression benchmark problems show that the introduction of the μ × λ EA and HGT events improve the performance of EGGP. Comparisons with Genetic Programming and Cartesian Genetic Programming strongly favour our proposed approach.
full paper PDF | doi:10.1145/3321707.3321788
@inproceedings(Atkinson++:2019:GECCO, author = "Timothy Atkinson and Detlef Plump and Susan Stepney", title = "Evolving graphs with horizontal gene transfer", pages = "968-976", doi = "10.1145/3321707.3321788", crossref = "GECCO:2019" ) @proceedings(GECCO:2019, title = "GECCO 2019, Prague, Czech Republic, July 2019", booktitle = "GECCO 2019, Prague, Czech Republic, July 2019", publisher = "ACM", year = 2019 )