- author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Stephan Winkler and Gabriel Kronberger",
- title = "Visualization of genetic lineages and inheritance information in genetic programming",
- booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion",
- year = "2013",
- editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks",
- isbn13 = "978-1-4503-1964-5",
- keywords = "genetic algorithms, genetic programming",
- pages = "1351--1358",
- month = "6-10 " # jul,
- organisation = "SIGEVO",
- address = "Amsterdam, The Netherlands",
-
DOI = "
doi:10.1145/2464576.2482714",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "Many studies emphasise the importance of genetic
diversity and the need for an appropriate tuning of
selection pressure in genetic programming. Additional
important aspects are the performance and effects of
the genetic operators (crossover and mutation) on the
transfer and stabilisation of inherited information
blocks during the run of the algorithm. In this
context, different ideas about the usage of lineage and
genealogical information for improving genetic
programming have taken shape in the last decade.
Our work builds on those ideas by introducing an evolution tracking framework for assembling genealogical and inheritance graphs of populations. The proposed approach allows detailed investigation of phenomena related to building blocks, size evolution, ancestry and diversity. We introduce the notion of genetic fragments to represent sub-trees that are affected by reproductive operators (mutation and crossover) and present a methodology for tracking such fragments using flexible similarity measures. A fragment matching algorithm was designed to work on both structural and semantic levels, allowing us to gain insight into the exploratory and exploitative behaviour of the evolutionary process.
The visualisation part which is the subject of this paper integrates with the framework and provides an easy way of exploring the population history. The paper focuses on a case study in which we investigate the evolution of a solution to a symbolic regression benchmark problem.",
- notes = "Also known as \cite{2482714} Distributed at GECCO-2013.",
