@incollection{Kotanchek:2006:GPTP,
title = {Pursuing the Pareto Paradigm Tournaments, Algorithm
Variations \& Ordinal Optimization},
address = {Ann Arbor},
author = {Mark Kotanchek and Guido Smits and Ekaterina Vladislavleva},
booktitle = {Genetic Programming Theory and Practice {IV}},
chapter = {12},
editor = {Rick L. Riolo and Terence Soule and Bill Worzel},
month = {11-13 May},
pages = {167--186},
publisher = {Springer},
series = {Genetic and Evolutionary Computation},
volume = {5},
year = {2006},
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.},
isbn = {0-387-33375-4}, notes = {part of \cite{Riolo:2006:GPTP} Published Jan 2007
after the workshop
},
keywords = {algorithms, genetic programming }
}
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