Genetic algorithms (GAs) have been used in many disciplines to optimize solutions for a
broad range of problems. In the last 20 years, the statistical literature has seen an increase …

Designing an experiment to fit a response surface model typically involves selecting among
several candidate designs. There are often many competing criteria that could be …

The fundamental principles of experiment design are factorization, replication,
randomization, and local control of error. In many industrial experiments, however, departure …

Electrochemical recovery of phosphorus from synthetic wastewater using a sacrificial Mg
anode has been explored as a function of key parameters, including pH, voltage, and …

We introduce a new algorithm for generating near G-optimal designs for second-order
models over cuboidal regions. The algorithm involves the use of Brent's minimization …

Three second-order response surface designs, namely: central composite design, small
composite design and minimum-run resolution V design are compared for factors 3 to 7 in …

Three readily available second-order response surface designs for a relatively large number
of factors are examined and compared. The number of factors in this study range from 6< k< …

Efficient estimation of response variables in a process is an important problem that requires
experimental designs appropriated for each specific situation. When we have a system …

A new MDO framework has been developed in Python to provide an ideal platform for
comparisons relating the performance of various MDO architectures. Specifically, it …

Experimental designs for a given task should be selected on the base of the problem being
solved and of some criteria that measure their quality. There are several such criteria …