It is Time for New Perspectives on How to Fight Bloat in GP

@inproceedings{Vega2019ItIT,
  title={It is Time for New Perspectives on How to Fight Bloat in GP},
  author={Francisco Fern{\'a}ndez de Vega and Gustavo Olague and O FranciscoCh{\'a}vezdela and Daniel Lanza and Wolfgang Banzhaf and Erik D. Goodman},
  booktitle={GPTP},
  year={2019}
}
The present and future of evolutionary algorithms depends on the proper use of modern parallel and distributed computing infrastructures. Although still sequential approaches dominate the landscape, available multi-core, many-core and distributed systems will make users and researchers to more frequently deploy parallel version of the algorithms. In such a scenario, new possibilities arise regarding the time saved when parallel evaluation of individuals are performed. And this time saving is… 
View PDF on arXiv
2 Citations
Background Citations
1
Methods Citations
1

2 Citations

Time and Individual Duration in Genetic Programming
TLDR
A new way of measuring complexity in variable-size-chromosome-based evolutionary algorithms by using computing time as a measure of individuals’ complexity allows control of the natural size growth of genetic programming individuals while preserving the quality of solutions in both the parallel and sequential versions of the algorithm.
On-the-fly simplification of genetic programming models
TLDR
Two techniques for simplifying the generated models of genetic programming are proposed and it is shown that they are capable of finding solutions at par with those generated by the standard GP algorithm - but with significantly reduced program size.

References

SHOWING 1-10 OF 23 REFERENCES
Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories
TLDR
Dynamic Limits is introduced, a new approach to bloat control that implements a dynamic limit that can be raised or lowered, depending on the best solution found so far, and can be applied either to the depth or size of the programs being evolved.
A Comparison of Bloat Control Methods for Genetic Programming
TLDR
This article augment depth limiting with nine bloat control methods and compares them with one another, identifying where each bloat Control method performs well on a per-problem basis, and under what settings various methods are effective independent of problem.
Implicitly Controlling Bloat in Genetic Programming
TLDR
By placing a population on a torus, with selection defined by a Moore neighborhood and local elitist replacement, bloat can be substantially reduced without compromising performance.
Control of Bloat in Genetic Programming by Means of the Island Model
TLDR
The Island Model helps to prevent the bloat phenomenon and is based in a well-known parallel evolutionary model: the island model.
A Preliminary Analysis and Simulation of Load Balancing Techniques Applied to Parallel Genetic Programming
TLDR
A preliminary study and simulation of some recently proposed load balancing techniques when applied to Parallel Genetic Programming, with conclusions that may be extended to any Parallel or Distributed Evolutionary Algorithm.
Measuring bloat, overfitting and functional complexity in genetic programming
TLDR
Three measures to respectively quantify bloat, overfitting and functional complexity of solutions and show their suitability on a set of test problems including a simple bidimensional symbolic regression test function and two real-life multidimensional regression problems are defined.
neat Genetic Programming: Controlling bloat naturally
Studying the influence of synchronous and asynchronous parallel GP on programs length evolution
TLDR
The synchronous and asynchronous version of the island-model in GP domain is studied, which shows an interesting link between the number of processes, subpopulations and the model the authors should use when applying parallelism to GP.
Parallel Genetic Programming and its Application to Trading Model Induction
Efficient use of computational resources in genetic programming: controlling the bloat phenomenon by means of the island model
TLDR
Whether island-based genetic programming can save computational effort by controlling the bloat phenomenon is studied and results show that this model allows to reduce the computation resources required for finding solutions when using genetic programming.
...
...