No abstract

No abstract

No abstract

This paper presents a comparison of the performance of a fuzzy logic controlled genetic algorithm (FLC-GA) and a parameter tuned genetic algorithm (TGA) for an agile manufacturing application. These strategies are benchmarked using a GA that utilizes a canonical static parameter set. In the FLC-GA, fuzzy logic controllers dynamically schedule parameters of the object-level GA. A fuzzy knowledge-base is automatically identified and tuned using a high-level GA. In the TGA, a high-level GA is used to determine an optimal static parameter set for the object-level GA. The object-level GA supports a global evolutionary optimization of design, manufacturing, and supplier planning decisions for manufacturing of printed circuit assemblies in an agile environment. We demonstrate that high-level system identification (for the FLC-G A) or tuning (for the TGA) performed with small object-level search spaces, can be successfully extended to more elaborate object-level search spaces, without employing additional identification or tuning. The TGA performs superior searches, but incurs large search times. The FLC-GA performs faster searches than a TGA, and is slower than the GA that utilizes a canonical static parameter set. However, search quality of the FLC-GA is comparable to that of the GA which utilizes a canonical static parameter set.

A principal challenge in modern computational finance is efficient portfolio designportfolio optimization followed by decision-making. Optimization based on even the widely used Markowitz two-objective mean-variance approach becomes computationally challenging for real-life portfolios. Practical portfolio design introduces further complexity as it requires the optimization of multiple return and risk measures subject to a variety of risk and regulatory constraints. Further, some of these measures may be nonlinear and nonconvex, presenting a daunting challenge to conventional optimization approaches. We introduce a powerful hybrid multiobjective optimization approach that combines evolutionary computation with linear programming to simultaneously maximize these return measures, minimize these risk measures, and identify the efficient frontier of portfolios that satisfy all constraints. We also present a novel interactive graphical decision-making method that allows the decision-maker to quickly down-select to a small subset of efficient portfolios. The approach has been tested on real-life portfolios with hundreds to thousands of assets, and is currently being used for investment decisionmaking in industry.