- title = "Coevolution of Fitness Predictors",
- author = "Michael D. Schmidt and Hod Lipson",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2008",
- month = dec,
- volume = "12",
- number = "6",
- pages = "736--749",
- keywords = "genetic algorithms, genetic programming, approximation theory, evolutionary computation, regression analysis accuracy loss, evolutionary progress, fitness evaluation cost reduction, fitness predictors, model approximation level, model learning investment, solution bloat reduction, symbolic regression problem",
- ISSN = "1089-778X",
-
DOI = "
doi:10.1109/TEVC.2008.919006",
- abstract = "We present an algorithm that coevolves fitness predictors, optimized for the solution population, which reduce fitness evaluation cost and frequency, while maintaining evolutionary progress. Fitness predictors differ from fitness models in that they may or may not represent the objective fitness, opening opportunities to adapt selection pressures and diversify solutions. The use of coevolution addresses three fundamental challenges faced in past fitness approximation research: 1) the model learning investment; 2) the level of approximation of the model; and 3) the loss of accuracy. We discuss applications of this approach and demonstrate its impact on the symbolic regression problem. We show that coevolved predictors scale favorably with problem complexity on a series of randomly generated test problems. Finally, we present additional empirical results that demonstrate that fitness prediction can also reduce solution bloat and find solutions more reliably.",
-
notes = "Also known as \cite{4476145} Three populations, pop
sizes = 128, 8, 10. Different representation and GA in
each. Comparsion with SAWs, GGGP, TAGP3
\cite{hoai:2002:stsrpwtgggptcr}, etc etc
\cite{DBLP:conf/sac/DolinBR02}.
",
