Modified genetic algorithms are developed and presented in this paper. Principles of
genetics and natural selection are adapted into the search procedure for mixed-discrete
nonlinear optimization problems. Such classes of global optimization algorithms are based
on a randomized selection of design space that yields an improvement in the objective
function. An implementation of the approach to a series of test problems in engineering
design optimization with diversity of variable representations and demonstrated
nonconvexities are discussed, and the results were compared with other algorithms. Results
show that genetic algorithms are able to consistently provide efficient, fine quality solutions,
that are robust to genetic parameters and provide a significant capability for mixed-discrete
constrained nonlinear optimization problems.
