GADO
# GADO: Genetic Algorithm for Design Optimization

In my Ph.D research, I developed GADO, a genetic algorithm (GA)
designed with the goal of being suitable for use in engineering
design. It uses new operators and search control strategies that
target the domains which typically arise in such applications. GADO
has been applied in a variety of optimization tasks which span many
fields. It demonstrated a great deal of robustness and efficiency
relative to competing methods.
In GADO, each individual in the GA population represents a parametric
description of an artifact, such as an aircraft or a missile. All
parameters have continuous intervals. The fitness of each individual
is based on the sum of a proper measure of merit computed by a
simulator or some analysis code (such as the takeoff mass of an
aircraft), and a penalty function if relevant (such as to impose
limits on the permissible size of an aircraft). A steady state GA
model is used, in which operators are applied to two parents selected
from the elements of the population via some selection scheme, one
offspring point is produced, then an existing point in the population
is replaced by the newly generated point via some replacement
strategy. Here selection was performed by rank because of the wide
range of fitness values caused by the use of a penalty function. The
replacement strategy used here is a crowding technique, which takes
into consideration both the fitness and the proximity of the points in
the GA population. The GA stops when either the maximum number of
evaluations has been exhausted or the population loses diversity and
practically converges to a single point in the search space. Floating
point representation is used. Several crossover and mutation
operators are used, most of which were designed specifically for the
target domain type. The most innovative crossover method is guided
crossover which emulates gradient based methods to improve the local
convergence of the GA. GADO also uses some search control strategies
such as a screening module which saves time by avoiding the full
evaluation of points that are unlikely to correspond to good designs.

More about GADO can be found in my publications.

## GADO Optimization Demos

Khaled Rasheed
(khaled@cs.uga.edu)