Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search

Conferences Papers
  1. Leonardo Trujillo Alejandra Serrano Trujillo Víctor H. Díaz Ramírez. Design of composite correlation filters for object recognition using multi-objective combinatorial optimization. 2013, 8856 - 8856 - 10. URL, DOI BibTeX

    @inproceedings{doi: 10.1117/12.2024597,
    	author = "Alejandra Serrano Trujillo,Víctor H. Díaz Ramírez,Leonardo Trujillo",
    	title = "Design of composite correlation filters for object recognition using multi-objective combinatorial optimization",
    	journal = "Proc.SPIE",
    	volume = 8856,
    	number = "",
    	pages = "8856 - 8856 - 10",
    	year = 2013,
    	doi = "10.1117/12.2024597",
    	url = "",
    	eprint = ""

Bloat is one of the most interesting theoretical problems in genetic programming (GP), and one of the most important pragmatic limitations in the development of real-world GP solutions. Over the years, many theories regarding the causes of bloat have been proposed and a variety of bloat control methods have been developed. It seems that one of the underlying causes of bloat is the search for fitness; as the fitness-causes-bloat theory states, selective bias towards fitness seems to unavoidably lead the search towards programs with a large size. Intuitively, however, abandoning fitness does not appear to be an option. This paper, studies a GP system that does not require an explicit fitness function, instead it relies on behavior-based search, where programs are described by the behavior they exhibit and selective pressure is biased towards unique behaviors using the novelty search algorithm. Initial results are encouraging, the average program size of the evolving population does not increase with novelty search; i.e., bloat is avoided by focusing on novelty instead of quality.

Published in
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV
Volume 227
Pages 293-305
Date of conference
10-13  July 2013
Last modified on%AM, %08 %235 %2013 %04:%Oct
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