- author = "Michael O'Neill and Anthony Brabazon",
- title = "Mutational Robustness and Structural Complexity in Grammatical Evolution",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "1338--1344",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, mutational Robustness, Complexity, Program Synthesis, PonyGE2",
- isbn13 = "978-1-7281-2152-6",
-
URL = "
http://ncra.ucd.ie/wp-content/uploads/2020/08/CEC2019___ComplexityRatchet.pdf",
-
code_url = "
https://github.com/PonyGE/PonyGE2",
-
DOI = "
doi:10.1109/CEC.2019.8790010",
- size = "7 pages",
- abstract = "A recent study in Artificial Life found that the need for mutational robustness can give rise to simpler structures in an evolving population. This begs the question, do we observe a similar phenomenon in Genetic Programming? Genetic Programming requires the search of structural space of solutions, usually requiring code growth to find fitter solutions. Typically Genetic Programming algorithms then suffer from code bloat, which is code growth in the absence of fitness gains. In this study we ask a simple question. Would the necessity for mutational robustness under selection pressure drive the evolution of less complex solution structures in Genetic Programming, with the potential to counteract code bloat?",
-
notes = "Helmuth and Spector Program Synthesis Benchmark
problem set \cite{Helmuth:2015:GECCO}
both mutation rates 'below the error threshold'.
'higher rate of mutation _phenotypic_ complexity is lower on all of the 8 harder problem'
'phenotypic complexity as the number of nodes in the derivation tree and the depth of the derivation tree'
Also known as \cite{8790010}
IEEE Catalog Number: CFP19ICE-ART",
