Age-Fitness Pareto Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Schmidt:2010:GPTP,

• author = "Michael Schmidt and Hod Lipson",
• title = "Age-Fitness Pareto Optimization",
• booktitle = "Genetic Programming Theory and Practice VIII",
• year = "2010",
• editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva",
• series = "Genetic and Evolutionary Computation",
• volume = "8",
• address = "Ann Arbor, USA",
• month = "20-22 " # may,
• publisher = "Springer",
• chapter = "8",
• pages = "129--146",
• keywords = "genetic algorithms, genetic programming, Symbolic Regression, Age, Fitness, Multi-objective",
• isbn13 = "978-1-4419-7746-5",
• URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
• DOI = "doi:10.1007/978-1-4419-7747-2_8",
• abstract = "We propose a multi-objective method, inspired by the Age Layered Population Structure algorithm, for avoiding premature convergence in evolutionary algorithms, and demonstrate a three-fold performance improvement over comparable methods. Previous research has shown that partitioning an evolving population into age groups can greatly improve the ability to identify global optima and avoid converging to local optima. Here, we propose that treating age as an explicit optimization criterion can increase performance even further, with fewer algorithm implementation parameters. The proposed method evolves a population on the two-dimensional Pareto front comprising (a) how long the genotype has been in the population (age); and (b) its performance (fitness). We compare this approach with previous approaches on the Symbolic Regression problem, sweeping the problem difficulty over a range of solution complexities and number of variables. Our results indicate that the multi-objective approach identifies the exact target solution more often than the age-layered population and standard population methods. The multi-objective method also performs better on higher complexity problems and higher dimensional datasets - finding global optima with less computational effort.",
• notes = "part of \cite{Riolo:2010:GPTP}",

}

Genetic Programming entries for Michael D Schmidt Hod Lipson

Citations