Using genetic algorithms for the single allocation hub location problem

Hub location problem is an NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. This work focuses on the Single Allocation Hub Location Problem (SAHLP). Genetic Algorithms (GAs) for the capacitated and uncapacitated variants of the SAHLP based on new chromosome representations and crossover operators are explored. The GAs is tested on two well-known sets of real-world problems with up to 200 nodes. The obtained results are very promising. For most of the test problems the GA obtains improved or best-known solutions and the computational time remains low. The proposed GAs can easily be extended to other variants of location problems arising in network design planning in transportation systems.

Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.

The Role of Experience in the Organization and Refinement of Face Space

Adults code faces in reference to category-specific norms that represent the different face categories encountered in the environment (e.g., race, age). Reliance on such norm-based coding appears to aid recognition, but few studies have examined the development of separable prototypes and the way in which experience influences the refinement of the coding dimensions associated with different face categories. The present dissertation was thus designed to investigate the organization and refinement of face space and the role of experience in shaping sensitivity to its underlying dimensions. In Study 1, I demonstrated that face space is organized with regard to norms that reflect face categories that are both visually and socially distinct. These results provide an indication of the types of category-specific prototypes that can conceivably exist in face space. Study 2 was designed to investigate whether children rely on category-specific prototypes and the extent to which experience facilitates the development of separable norms. I demonstrated that unlike adults and older children, 5-year-olds rely on a relatively undifferentiated face space, even for categories with which they receive ample experience. These results suggest that the dimensions of face space undergo significant refinement throughout childhood; 5 years of experience with a face category is not sufficient to facilitate the development of separable norms. In Studies 3 through 5, I examined how early and continuous exposure to young adult faces may optimize the face processing system for the dimensions of young relative to older adult faces. In Study 3, I found evidence for a young adult bias in attentional allocation among young and older adults. However, whereas young adults showed an own-age recognition advantage, older adults exhibited comparable recognition for young and older faces. These results suggest that despite the significant experience that older adults have with older faces, the early and continuous exposure they received with young faces continues to influence their recognition, perhaps because face space is optimized for young faces. In Studies 4 and 5, I examined whether sensitivity to deviations from the norm is superior for young relative to older adult faces. I used normality/attractiveness judgments as a measure of this sensitivity; to examine whether biases were specific to norm-based coding, I asked participants to discriminate between the same faces. Both young and older adults were more accurate when tested with young relative to older faces—but only when judging normality. Like adults, 3- and 7-year-olds were more accurate in judging the attractiveness of young faces; however, unlike adults, this bias extended to the discrimination task. Thus by 3 years of age children are more sensitive to differences among young relative to older faces, suggesting that young children's perceptual system is more finely tuned for young than older adult faces. Collectively, the results of this dissertation help elucidate the development of category-specific norms and clarify the role of experience in shaping sensitivity to the dimensions of face space.