@article{foster_turner_ferguson_donndelinger_2014, title={Creating targeted initial populations for genetic product searches in heterogeneous markets}, volume={46}, ISSN={["1029-0273"]}, DOI={10.1080/0305215x.2013.861458}, abstractNote={Genetic searches often use randomly generated initial populations to maximize diversity and enable a thorough sampling of the design space. While many of these initial configurations perform poorly, the trade-off between population diversity and solution quality is typically acceptable for small-scale problems. Navigating complex design spaces, however, often requires computationally intelligent approaches that improve solution quality. This article draws on research advances in market-based product design and heuristic optimization to strategically construct ‘targeted’ initial populations. Targeted initial designs are created using respondent-level part-worths estimated from discrete choice models. These designs are then integrated into a traditional genetic search. Two case study problems of differing complexity are presented to illustrate the benefits of this approach. In both problems, targeted populations lead to computational savings and product configurations with improved market share of preferenc...}, number={12}, journal={ENGINEERING OPTIMIZATION}, author={Foster, Garrett and Turner, Callaway and Ferguson, Scott and Donndelinger, Joseph}, year={2014}, month={Dec}, pages={1729–1747} }
 @inproceedings{foster_denhart_ferguson_2014, title={Effects of feedback on design space exploration}, DOI={10.1115/detc2013-13243}, abstractNote={The ultimate goal of this research is to provide computer based educational software that exposes engineering students to design tradeoffs early in their undergraduate experience. This paper investigates two feedback elements for their ability to enhance those students’ understanding of the tradeoffs inherent in a water rocket propulsion design problem: 1) a Latin hypercube sample that allows the student to select a starting point and 2) sensitivity values that displayed local gradient information. Assessments are made using data logged during the students’ interaction with the software and a series of quizzes performed throughout the study. The results indicate that the sensitivity information improves the students’ ability to locate designs with good performance, while the Latin hypercube adversely affects the students’ ability to visualize the objective space.}, booktitle={Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2013, vol 1}, author={Foster, G. and Denhart, J. and Ferguson, S.}, year={2014} }
 @inproceedings{foster_ferguson_2014, title={Enhanced targeted initial populations for multiobjective product line optimization}, DOI={10.1115/detc2013-13303}, abstractNote={Initial populations for genetic algorithms are often created using randomly generated designs in an effort to maximize the genetic diversity in the design space. However, research indicates that the inclusion of solutions generated based on domain knowledge (i.e. non-random solutions) can notably improve the performance of the genetic algorithm with respect to solution performance and/or computational cost for convergence. This performance increase is extremely valuable for computationally expensive problems, such as product line optimization. In prior research, the authors demonstrated these improvements for product line design problems where market share of preference was the performance objective. Initial product line solutions were constructed from products that had the largest product-level utility for individual respondents. However, this simple product identification strategy did not adequately scale to accommodate the richer design problem associated with multiple objectives. This paper extends the creation of targeted initial populations to multiobjective product line design problems by using the objectives of the problem, instead of product level utility, to identify candidate designs. A MP3 player and vehicle feature packaging product line design problems are used to demonstrate this approach and assess the improvement of this modification.}, booktitle={Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2013, vol 3A}, author={Foster, G. and Ferguson, S.}, year={2014} }
 @article{foster_ferguson_2013, title={Exploring the Effectiveness of Using Graveyard Data When Generating Design Alternatives}, volume={13}, ISSN={["1944-7078"]}, DOI={10.1115/1.4024913}, abstractNote={The objective of this paper is to demonstrate that unique alternative designs can be efficiently found by searching the discarded data (or graveyard) from a multiobjective genetic algorithm (MOGA). Motivation for using graveyard data to generate design alternatives arises from the computational cost associated with real-time design space exploration of multiobjective optimization problems. The effectiveness of this approach is explored by comparing (1) the uniqueness of alternatives found using graveyard data and those generated using an optimization-based search, and (2) how alternative generation near the Pareto frontier is impacted. Two multiobjective case study problems are introduced—a two bar truss and an I-beam design optimization. Results from these studies indicate that using graveyard data allows for the discovery of alternative designs that are at least 70% as unique as alternatives found using an optimization-based alternative identification approach, while saving a significant number of functional evaluations. Additionally, graveyard data are shown to be better suited for alternative generation near the Pareto frontier than standard sampling techniques. Finally, areas of future work are also discussed.}, number={4}, journal={JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING}, author={Foster, Garrett and Ferguson, Scott}, year={2013}, month={Dec} }
 @inproceedings{foster_ferguson_2012, title={Assessing the effectiveness of using graveyard data for generating design alternatives}, DOI={10.1115/detc2011-48636}, abstractNote={Modeling to Generate Alternatives (MGA) is a technique used to identify variant designs that maximize design space distance from an initial point while satisfying performance loss constraints. Recent work has explored the application of this technique to nonlinear design problems, where the design space was investigated using an exhaustive sampling procedure. While computational cost concerns were noted, the main focus was determining how scaling and distance metric selection influenced alternative discovery. To increase the viability of MGA for engineering design problems, this work looks to reduce the computational overhead needed to identify design alternatives. This paper investigates and quantifies the effectiveness of using previously sampled designs, i.e. a graveyard, from a multiobjective genetic algorithm as a means of reducing computational expense. Computational savings and the expected error are quantified to assess the effectiveness of this approach. These results are compared to other more common “search” techniques; namely Latin hypercube samplings, grid search, and the Nelder-Mead simplex method. The performance of these “search” techniques are subsequently explored in two case study problems — the design of a two bar truss, and an I-beam — to find the most unique alternative design over a range of different thresholds. Results from this work show the graveyard can be used as a way of inexpensively generating alternatives that are close to ideal, especially nearer to the starting design. Additionally, this paper demonstrates that graveyard information can be used to increase the performance of the Nelder-Mead simplex method when searching for alternative designs.}, booktitle={Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2011, vol 5, pts A and B}, author={Foster, G. and Ferguson, S.}, year={2012}, pages={563–576} }
 @inproceedings{foster_holland_ferguson_deluca_2012, title={The creation of design modules for use in engineering design education}, DOI={10.1115/detc2012-71181}, abstractNote={Industry demands that graduating engineers possess the ability to solve complex problems requiring multidisciplinary approaches and systems-level thinking. Unfortunately, current curricula often focus on analytical approaches to problem solving. Further, adding courses focused solely on engineering design is often unachievable due to the large amount of material covered in today’s undergraduate engineering curricula. Combined, these prevent a comprehensive focus on engineering design education from being realized. To overcome these time and resource constraints, this paper proposes the use of computational modules within current courses. The investigators hypothesize that the modules would eliminate the repetitive analysis barrier in design problems, thus allowing for design-related experiences to be included earlier in the curricula as opposed to postponing it to a capstone experience. Four major hurdles that hinder successful integration of modules in current engineering courses are: a) engaging students such that they will want to use the modules; b) ensuring the modules are easy to use; c) reducing the complexity of deploying the modules into the classroom; and d) providing educational value. To address these issues, this paper treats the design of the modules as a product design problem. This paper presents the redesign process followed to improve two different design modules planned for implementation in the engineering curriculum at North Carolina State University. Additionally, this research indicates that using a formal redesign process enhances a module’s ability to overcome the hurdles listed above.}, booktitle={Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol 7}, author={Foster, G. and Holland, M. and Ferguson, S. and Deluca, W.}, year={2012}, pages={23–36} }