- author = "Mark Kotanchek and Guido Smits and Ekaterina Vladislavleva",
- title = "Pursuing the Pareto Paradigm Tournaments, Algorithm Variations \& Ordinal Optimization",
- booktitle = "Genetic Programming Theory and Practice {IV}",
- year = "2006",
- editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
- volume = "5",
- series = "Genetic and Evolutionary Computation",
- pages = "167--185",
- address = "Ann Arbor",
- month = "11-13 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "0-387-33375-4",
-
DOI = "
doi:10.1007/978-0-387-49650-4_11",
-
abstract = "The ParetoGP algorithm, which adopts a multi-objective
optimisation approach to balancing expression
complexity and accuracy, has proven to have significant
impact on symbolic regression of industrial data due to
its improvement in speed and quality of model
development as well as user model selection.
In this chapter, we explore a range of topics related to exploiting the Pareto paradigm. First we describe and explore the strengths and weaknesses of the ClassicGP and ParetoGP variants for symbolic regression as well as touch on related algorithms.
Next, we show a derivation for the selection intensity of tournament selection with multiple winners (albeit, in a single-objective case). We then extend classical tournament and elite selection strategies into a multi-objective framework which allows classical GP schemes to be readily Pareto-aware.
Finally, we introduce the latest extension of the Pareto paradigm which is the melding with ordinal optimization. It appears that ordinal optimisation will provide a theoretical foundation to guide algorithm design. Application of these insights has already produced at least a four-fold improvement in the performance for a suite of test problems.",
-
notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007
after the workshop
",
