TY - GEN
T1 - Lexi2
T2 - 2022 Genetic and Evolutionary Computation Conference, GECCO 2022
AU - De Lima, Allan
AU - Carvalho, Samuel
AU - Dias, Douglas Mota
AU - Naredo, Enrique
AU - Sullivan, Joseph P.
AU - Ryan, Conor
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/7/8
Y1 - 2022/7/8
N2 - Bloat, a well-known phenomenon in Evolutionary Computation, often slows down evolution and complicates the task of interpreting the results. We propose Lexi2, a new selection and bloat-control method, which extends the popular lexicase selection method, by including a tie-breaking step which considers attributes related to the size of the individuals. This new step applies lexicographic parsimony pressure during the selection process and is able to reduce the number of random choices performed by lexicase selection (which happen when more than a single individual correctly solve the selected training cases). Furthermore, we propose a new Grammatical Evolution-specific, low-cost diversity metric based on the grammar mapping modulus operations remainders, which we then utilise with Lexi2. We address four distinct problems, and the results show that Lexi2 is able to reduce significantly the length, the number of nodes and the depth for all problems, to maintain a high level of diversity in three of them, and to significantly improve the fitness score in two of them. In no case does it adversely impact the fitness.
AB - Bloat, a well-known phenomenon in Evolutionary Computation, often slows down evolution and complicates the task of interpreting the results. We propose Lexi2, a new selection and bloat-control method, which extends the popular lexicase selection method, by including a tie-breaking step which considers attributes related to the size of the individuals. This new step applies lexicographic parsimony pressure during the selection process and is able to reduce the number of random choices performed by lexicase selection (which happen when more than a single individual correctly solve the selected training cases). Furthermore, we propose a new Grammatical Evolution-specific, low-cost diversity metric based on the grammar mapping modulus operations remainders, which we then utilise with Lexi2. We address four distinct problems, and the results show that Lexi2 is able to reduce significantly the length, the number of nodes and the depth for all problems, to maintain a high level of diversity in three of them, and to significantly improve the fitness score in two of them. In no case does it adversely impact the fitness.
KW - grammatical evolution
KW - lexicase selection
KW - lexicographic parsimony pressure
UR - http://www.scopus.com/inward/record.url?scp=85135239494&partnerID=8YFLogxK
U2 - 10.1145/3512290.3528803
DO - 10.1145/3512290.3528803
M3 - Conference contribution
AN - SCOPUS:85135239494
T3 - GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
SP - 929
EP - 937
BT - GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery, Inc
Y2 - 9 July 2022 through 13 July 2022
ER -