TY - GEN
T1 - Performance Upgrade of Sequence Detector Evolution Using Grammatical Evolution and Lexicase Parent Selection Method
AU - Majeed, Bilal
AU - Carvalho, Samuel
AU - Dias, Douglas Mota
AU - Youssef, Ayman
AU - Murphy, Aidan
AU - Ryan, Conor
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Quickly designing correct and efficient digital circuits is a crucial need for the electronics industry. Several Electronic Design Automation tools are used for this task. Still, they often lack the diversity of designs that search-based techniques can offer, such as our system producing three different designs for a 5-bit ‘11011’ Sequence Detector. Sequence Detectors are some of the most crucial digital sequential circuits evolved in this work using Grammatical Evolution, a Machine Learning technique based on Evolutionary Computation. Compared to the literature, a reasonably small training data set is used to generate diverse solutions/circuits. A comparison is delivered of the results of the evolved circuits using two different parent selection techniques, tournament selection and lexicase selection. It is shown that the evolved circuits using a small training data set have shown a hundred percent test accuracy on a vast amount of test data sets, and the performance of lexicase selection is much better than tournament selection while evolving these circuits.
AB - Quickly designing correct and efficient digital circuits is a crucial need for the electronics industry. Several Electronic Design Automation tools are used for this task. Still, they often lack the diversity of designs that search-based techniques can offer, such as our system producing three different designs for a 5-bit ‘11011’ Sequence Detector. Sequence Detectors are some of the most crucial digital sequential circuits evolved in this work using Grammatical Evolution, a Machine Learning technique based on Evolutionary Computation. Compared to the literature, a reasonably small training data set is used to generate diverse solutions/circuits. A comparison is delivered of the results of the evolved circuits using two different parent selection techniques, tournament selection and lexicase selection. It is shown that the evolved circuits using a small training data set have shown a hundred percent test accuracy on a vast amount of test data sets, and the performance of lexicase selection is much better than tournament selection while evolving these circuits.
KW - Electronic Design Automation
KW - Evolvable Hardware
KW - Grammatical Evolution
KW - Lexicase Selection
KW - Sequence Detector
UR - http://www.scopus.com/inward/record.url?scp=85177231498&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-44355-8_7
DO - 10.1007/978-3-031-44355-8_7
M3 - Conference contribution
AN - SCOPUS:85177231498
SN - 9783031443541
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 90
EP - 103
BT - Complex Computational Ecosystems - 1st International Conference, CCE 2023, Proceedings
A2 - Collet, Pierre
A2 - El Zant, Samer
A2 - Gardashova, Latafat
A2 - Abdulkarimova, Ulviya
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Complex Computational Ecosystems, CCE 2023
Y2 - 25 April 2023 through 27 April 2023
ER -