Inverse multi-objective robust evolutionary design
Genetic Programming and Evolvable Machines, 7 (4):
383--404 (December 2006)DOI: doi:10.1007/s10710-006-9013-7
Abstract
we present an Inverse Multi-Objective Robust
Evolutionary (IMORE) design methodology that handles
the presence of uncertainty without making assumptions
about the uncertainty structure. We model the
clustering of uncertain events in families of nested
sets using a multi-level optimisation search. To reduce
the high computational costs of the proposed
methodology we proposed schemes for (1) adapting the
step-size in estimating the uncertainty, and (2)
trimming down the number of calls to the objective
function in the nested search. Both offline and online
adaptation strategies are considered in conjunction
with the IMORE design algorithm. Design of Experiments
(DOE) approaches further reduce the number of objective
function calls in the online adaptive IMORE algorithm.
Empirical studies conducted on a series of test
functions having diverse complexities show that the
proposed algorithms converge to a set of Pareto-optimal
design solutions with non-dominated nominal and
robustness performances efficiently.