Multiobjective genetic algorithms with application to control engineering problems.

Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number
of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control engineering, they have
found application mainly in problems involving functions difficult to characterize
mathematically or known to present difficulties to more conventional numerical
optimizers, as well as problems involving non-numeric and mixed-type variables.
In addition, they exhibit a large degree of parallelism, making it possible to effectively exploit the computing power made available through parallel processing.
Despite their early recognized potential for multiobjective optimization (almost all engineering problems involve multiple, often conflicting objectives), genetic algorithms have, for the most part, been applied to aggregations of the
objectives in a single-objective fashion, like conventional optimizers. Although
alternative approaches based on the notion of Pareto-dominance have been suggested, multiobjective optimization with genetic algorithms has received comparatively little attention in the literature.
In this work, multiobjective optimization with genetic algorithms is reinterpreted as a sequence of decision making problems interleaved with search steps,
in order to accommodate previous work in the field. A unified approach to multiple objective and constraint handling with genetic algorithms is then developed
from a decision making perspective and characterized, with application to control system design in mind. Related genetic algorithm issues, such as the ability
to maintain diverse solutions along the trade-off surface and responsiveness to
on-line changes in decision policy, are also considered.
The application of the multiobjective GA to three realistic problems in optimal controller design and non-linear system identification demonstrates the ability of the approach to concurrently produce many good compromise solutions in a
single run, while making use of any preference information interactively supplied
by a human decision maker. The generality of the approach is made clear by the
very different nature of the two classes of problems considered.