- author = "Raja Muhammad Atif Azad and Conor Ryan",
- title = "A Simple Approach to Lifetime Learning in Genetic Programming based Symbolic Regression",
- journal = "Evolutionary Computation",
- year = "2014",
- volume = "22",
- number = "2",
- pages = "287--317",
- month = "Summer",
- keywords = "genetic algorithms, genetic programming, hill climbing, Lamarckian, genetic repair, Memetic Algorithms, lifetime learning, local search, hybrid genetic algorithms",
- ISSN = "1063-6560",
-
URL = "
http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00111",
-
URL = "https://pure.ul.ie/en/publications/a-simple-approach-to-lifetime-learning-in-genetic-programming-bas",
-
URL = "https://ref2021-resultsapp-live.azurewebsites.net/outputs/6b1e8c2e-083e-4fc5-9f66-10dc5172e041",
-
DOI = "
doi:10.1162/EVCO_a_00111",
- size = "31 pages",
-
abstract = "Genetic Programming (GP) coarsely models natural
evolution to evolve computer programs. Unlike in
nature, where individuals can often improve their
fitness through lifetime experience, the fitness of GP
individuals generally does not change during their
lifetime, and there is usually no opportunity to pass
on acquired knowledge.
This paper introduces the Chameleon system to address this discrepancy and augment GP with lifetime learning by adding a simple local search that operates by tuning the internal nodes of individuals. Although not the first attempt to combine local search with GP, its simplicity means that it is easy to understand and cheap to implement.
A simple cache is added which leverages the local search to reduce the tuning cost to a small fraction of the expected cost, and we provide a theoretical upper limit on the maximum tuning expense given the average tree size of the population and show that this limit grows very conservatively as the average tree size of the population increases.
We show that Chameleon uses available genetic material more efficiently by exploring more actively than with standard GP, and demonstrate that not only does Chameleon outperform standard GP (on both training and test data) over a number of symbolic regression type problems, it does so by producing smaller individuals and that it works harmoniously with two other well known extensions to GP, namely, linear scaling and a diversity-promoting tournament selection method.",
-
notes = "Chameleon, cache, UCI, depth first hill climbing,
internal nodes (functions) only.
Disappeared Sep 2014 http://casnew.iti.upv.es/index.php/evocompetitions/105-symregcompetition",
