- author = "Dara Curran and Eugene Freuder and Thomas Jansen",
- title = "Incremental evolution of local search heuristics",
- booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation",
- year = "2010",
- editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt",
- isbn13 = "978-1-4503-0072-8",
- pages = "981--982",
- keywords = "genetic algorithms, genetic programming, incremental evolution, genetic programming, local search heuristics, graph colouring, hyperheuristics, Poster",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Portland, Oregon, USA",
-
DOI = "
doi:10.1145/1830483.1830660",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "In evolutionary computation, incremental evolution refers to the process of employing an evolutionary environment that becomes increasingly complex over time. We present an implementation of this approach to develop randomised local search heuristics for constraint satisfaction problems, combining research on incremental evolution with local search heuristics evolution. A population of local search heuristics is evolved using a genetic programming framework on a simple problem for a short period and is then allowed to evolve on a more complex problem. Experiments compare the performance of this population with that of a randomly initialised population evolving directly on the more complex problem. The results obtained show that incremental evolution can represent a significant improvement in terms of optimisation speed, solution quality and solution structure.",
-
notes = "Culberon's random graph generator.
Also known as \cite{1830660} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)",
