genetic algorithm

Uncertainty is inevitable in engineering design optimization and can significantly degrade the performance of an optimized design solution and/or even change feasibility by making a feasible solution infeasible. The problem with uncertainty can be exacerbated in multi-disciplinary optimization whereby the models for several disciplines are coupled and the propagation of uncertainty has to be accounted for within and across disciplines. It is important to determine which ranges of parameter uncertainty are most important or how to best allocate investments to ...

The high computational cost of population based optimization methods has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise approaches that can significantly reduce the number of function (or simulation) calls required in such optimization methods. This dissertation presents some new online and offline approximation approaches for design optimization. In particular, it presents new DOE and metamodeling techniques for Genetic Algorithm (GA) based multi-objective optimization methods along four research thrusts. The first ...

Genetic algorithms have a lot of properties that makes it a good choice when one needs to solve very complicated problems. The performance of genetic algorithms is affected by the parameters that are used. Optimization of the parameters for the genetic algorithm is one of the most popular research fields of genetic algorithms. One of the reasons for this is because of the complicated relation between the parameters and factors such as the complexity of the problem. This thesis describes ...

This thesis investigates the costs associated with a bus scheduling problem in an urban transit network for both deterministic and stochastic arrival processes and proposes computerized models for each. A simple genetic algorithm (SGA) with some problem-specific genetic operators is developed for the deterministic arrival process and a simulation-based genetic algorithm (SBGA) is developed for the stochastic arrival process. The new models are applied to an artificial bus network to test their efficiency. Several sensitivity analyses and a goodness test ...

Significant recent research has focused on the marriage of consumer preferences and engineering design in order to improve profitability. However, in many markets, the profitability of new products for manufacturers is also a significant function of the retail channel structure through which the new products reach the ultimate customer. At the crux of the issue is the fact that channel dominating retailers, like Home Depot, Toys R' Us, Wal-Mart have significant power arising from their hundreds of billions of dollars ...

For electronic systems it is not uncommon for 60% or more of the recurring cost to be associated with testing. Performing tradeoffs associated with where in a process to test and what level of test, diagnosis and rework to perform are key to optimizing the cost and yield of an electronic system's assembly. In this dissertation, a methodology that uses a real-coded genetic algorithm has been developed to minimize the yielded cost of electronic products by optimizing the locations of ...

We present a study of two NP-hard telecommunications network design problems - the prize-collecting generalized minimum spanning tree problem (PCGMST) and the design of optical networks with wavelength division multiplexing. The first problem, the PCGMST problem, involves the design of regional backbone networks, where a set of local area networks (LANs) need to be connected by a minimum cost tree network using exactly one gateway site from each LAN. We present several polynomial time heuristics for the PCGMST problem and ...