Genetic algorithms (GAs) are randomized search and optimization techniques
guided by the principles of evolution and natural genetics; they have a large
amount of implicit parallelism. GAs perform multimodal search in complex
landscapes and provide near-optimal solutions for objective or tness func-
tion of an optimization problem. They have applications in elds as diverse
as pattern recognition, image processing, neural networks, machine learning,
jobshop scheduling and VLSI design, to mention just a few.
Traditionally, GAs were designed to solve problems with an objective to
optimize only a single criterion. However, many real-life problems involve mul-
tiple con
icting measures of performance, or objectives, which need simulta-
neous optimization. Optimal performance according to one objective, if such
an optimum exists, often implies unacceptably low performance in one or
more of the other objective dimensions, creating the need for a compromise
to be reached. A suitable set of solutions, called the Pareto-optimal set, to
such problems is one where none of the solutions can be further improved on
any one objective without degrading it in another. In recent times, there has
been a spurt of activities in exploiting the signicantly powerful search capa-
bility of GAs for multiobjective optimization, leading to the development of
several algorithms belonging to the class of multiobjective genetic algorithms
(MOGAs).