- author = "Aliyu Sani Sambo and R. Muhammad Atif Azad and Yevgeniya Kovalchuk and Vivek Padmanaabhan Indramohan and Hanifa Shah",
- title = "Leveraging Asynchronous Parallel Computing to Produce Simple Genetic Programming Computational Models",
- booktitle = "The 35th ACM/SIGAPP Symposium On Applied Computing",
- year = "2020",
- editor = "Federico Divina and Miguel Garcia Torres",
- pages = "521--528",
- address = "Brno, Czech Republic",
- month = mar # " 30 -- " # apr # " 3",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1145/3341105.3373921",
-
abstract = "Traditionally, reducing complexity in Machine Learning
promises benefits such as less overfitting. However,
complexity control in Genetic Programming (GP) often
means reducing the sizes of the evolving expressions,
and past literature shows that size reduction does not
necessarily reduce overfitting. In fact, whether size
consistently represents complexity is itself debatable.
Therefore, this paper proposes evaluation time of an
evolving model - the computational time required to
evaluate a model on data - as the estimate of its
complexity. Evaluation time depends upon the size, but
crucially also on the composition of an evolving model,
and can thus distil its underlying complexity. To
discourage complexity, this paper takes an innovative
approach that asynchronously evaluates multiple models
concurrently. These models race to their completion;
thus, those models that finish earlier, join the
population earlier to breed further in a steady-state
fashion. Thus, the computationally simpler models, even
if less accurate, get further chances to evolve before
the more accurate yet expensive models join the
population. Crucially, since evaluation times vary from
one execution to another, this paper also shows how to
significantly minimise this variation.
The paper compares the proposed method on six challenging symbolic regression problems with both standard GP and GP with an effective bloat control method. The results demonstrated that the proposed asynchronous parallel GP (APGP) indeed produces individuals that are smaller, faster and more accurate than those in standard GP. While GP with bloat control (GP+BC) produced smaller individuals, it did so at the cost of lower accuracy than APGP both on training and test data, thus questioning the overall benefits of bloat control. Also, while APGP took the fewest evaluations to match the training accuracy of GP, GP+BC took the most.
These results, and the portability of evaluation time as an estimate of complexity encourage further experimentation and fine-tuning of this hitherto unexplored style of GP.",
-
notes = "Applications of Evolutionary Computing
Track
Birmingham City University, UK
https://www.sigapp.org/sac/sac2020/program.html",
