TY - GEN
T1 - GEMO
T2 - 12th International Joint Conference on Computational Intelligence, IJCCI 2020
AU - Kshirsagar, Meghana
AU - Jachak, Rushikesh
AU - Chaudhari, Purva
AU - Ryan, Conor
N1 - Publisher Copyright:
Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
PY - 2020
Y1 - 2020
N2 - In Grammatical Evolution (GE) individuals occupy more space than required, that is, the Actual Length of the individuals is longer than their Effective Length. This has major implications for scaling GE to complex problems that demand larger populations and complex individuals. We show how these two lengths vary for different sizes of population, demonstrating that Effective Length is relatively independent of population size, but that the Actual Length is proportional to it. We introduce Grammatical Evolution Memory Optimization (GEMO), a two-stage evolutionary system that uses a multi-objective approach to identify the optimal, or at least, near-optimal, genome length for the problem being examined. It uses a single run with a multi-objective fitness function defined to minimize the error for the problem being tackled along with maximizing the ratio of Effective to Actual Genome Length leading to better utilization of memory and hence, computational speedup. Then, in Stage 2, standard GE runs are performed restricting the genome length to the length obtained in Stage 1. We demonstrate this technique on different problem domains and show that in all cases, GEMO produces individuals with the same fitness as standard GE but significantly improves memory usage and reduces computation time.
AB - In Grammatical Evolution (GE) individuals occupy more space than required, that is, the Actual Length of the individuals is longer than their Effective Length. This has major implications for scaling GE to complex problems that demand larger populations and complex individuals. We show how these two lengths vary for different sizes of population, demonstrating that Effective Length is relatively independent of population size, but that the Actual Length is proportional to it. We introduce Grammatical Evolution Memory Optimization (GEMO), a two-stage evolutionary system that uses a multi-objective approach to identify the optimal, or at least, near-optimal, genome length for the problem being examined. It uses a single run with a multi-objective fitness function defined to minimize the error for the problem being tackled along with maximizing the ratio of Effective to Actual Genome Length leading to better utilization of memory and hence, computational speedup. Then, in Stage 2, standard GE runs are performed restricting the genome length to the length obtained in Stage 1. We demonstrate this technique on different problem domains and show that in all cases, GEMO produces individuals with the same fitness as standard GE but significantly improves memory usage and reduces computation time.
KW - Autoregressive time series forecasting
KW - Evolutionary computation
KW - Grammatical evolution
KW - Memory optimization
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85103858583&partnerID=8YFLogxK
U2 - 10.5220/0010106501840191
DO - 10.5220/0010106501840191
M3 - Conference contribution
AN - SCOPUS:85103858583
SN - 9789897584756
T3 - IJCCI 2020 - Proceedings of the 12th International Joint Conference on Computational Intelligence
SP - 184
EP - 191
BT - IJCCI 2020 - Proceedings of the 12th International Joint Conference on Computational Intelligence
A2 - Merelo, Juan Julian
A2 - Garibaldi, Jonathan
A2 - Wagner, Christian
A2 - Back, Thomas
A2 - Madani, Kurosh
A2 - Warwick, Kevin
PB - SciTePress
Y2 - 2 November 2020 through 4 November 2020
ER -