TY - GEN
T1 - Leap mapping
T2 - 2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion
AU - de Lima, Allan
AU - Carvalho, Samuel
AU - Dias, Douglas Mota
AU - Sullivan, Joseph P.
AU - Ryan, Conor
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s).
PY - 2023/7/15
Y1 - 2023/7/15
N2 - We introduce Leap mapping, a new mapping process for Grammatical Evolution (GE), which spreads introns within the effective length of the genome (the part of the genome consumed while mapping), preserving information for future generations and performing less disruptive crossover and mutation operations than standard GE. Using the exact same genotypic representation as GE, Leap mapping reads the genome in separate parts named ‘frames’, where the size of each is the number of production rules in the grammar. Each codon inside a frame is responsible for mapping a different production rule of the grammar. The process keeps consuming codons from the frame until it needs to map again a production rule already mapped with that frame. At this point, the mapping starts consuming codons from the next frame. We assessed the performance of this new mapping in some benchmark problems, which require modular solutions: four Boolean problems and three versions of the Lawnmower problem. Moreover, we compared the results with the standard mapping procedure and a multi-genome version.
AB - We introduce Leap mapping, a new mapping process for Grammatical Evolution (GE), which spreads introns within the effective length of the genome (the part of the genome consumed while mapping), preserving information for future generations and performing less disruptive crossover and mutation operations than standard GE. Using the exact same genotypic representation as GE, Leap mapping reads the genome in separate parts named ‘frames’, where the size of each is the number of production rules in the grammar. Each codon inside a frame is responsible for mapping a different production rule of the grammar. The process keeps consuming codons from the frame until it needs to map again a production rule already mapped with that frame. At this point, the mapping starts consuming codons from the next frame. We assessed the performance of this new mapping in some benchmark problems, which require modular solutions: four Boolean problems and three versions of the Lawnmower problem. Moreover, we compared the results with the standard mapping procedure and a multi-genome version.
KW - Grammatical Evolution
KW - introns
KW - mapping
UR - http://www.scopus.com/inward/record.url?scp=85169058956&partnerID=8YFLogxK
U2 - 10.1145/3583133.3590680
DO - 10.1145/3583133.3590680
M3 - Conference contribution
AN - SCOPUS:85169058956
T3 - GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
SP - 555
EP - 558
BT - GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
Y2 - 15 July 2023 through 19 July 2023
ER -