Bounding Bloat in Genetic Programming

While many optimization problems work with a fixed number of decision variables and thus a fixed-length representation of possible solutions, genetic programming (GP) works on variable-length representations. A naturally occurring problem is that of bloat (unnecessary growth of solutions) slowing down optimization. Theoretical analyses could so far not bound bloat and required explicit assumptions on the magnitude of bloat. In this paper we analyze bloat in mutation-based genetic programming for the two test functions ORDER and MAJORITY. We overcome previous assumptions on the magnitude of bloat and give matching or close-to-matching upper and lower bounds for the expected optimization time. In particular, we show that the (1+1) GP takes (i) $\Theta(T_{init} + n \log n)$ iterations with bloat control on ORDER as well as MAJORITY; and (ii) $O(T_{init} \log T_{init} + n (\log n)^3)$ and $\Omega(T_{init} + n \log n)$ (and $\Omega(T_{init} \log T_{init})$ for $n=1$) iterations without bloat control on MAJORITY.Comment: An extended abstract has been published at GECCO 201

The Transfer of ‘International Best Practice’ in a Brazilian MNC: A Consideration of the Convergence and Contingency Perspectives

This study examines the transfer of a Brazilian MNC's HR model to its subsidiaries in the UK, Canada, Switzerland and Norway. It enquires where the model was sourced from, to what extent it bore a distinct Brazilian complexion, and whether it was adapted to meet the strictures of host institutional constraints and traditions. The paper uses these questions to address an important theoretical debate in the international business literature; that is, whether the pattern of diffusion of management practices within MNCs will lead to a convergence of practices across companies and countries à la the convergence perspective, or whether this is unlikely given the variety of social and political constraints limiting such a process as suggested by the contingency perspective. We find that the MNC imposed a unitary (US-sourced) model of HR ‘best practice’ on all of its subsidiaries. Thus our empirical findings support the convergence thesis. However, we argue that these outcomes are largely explained by relations of power and economic dependence; specifically, the co-existence of dominant-country (US) practices and a dominant sectoral firm operating in economically dependent regions. Where similar circumstances are replicated one might foresee convergence within sectors across countries, but otherwise pluralism and eclecticism between sectors and across countries might be the predominant pattern along the lines envisaged in the conceptualization of “converging divergences”

Geometric Inhomogeneous Random Graphs

Real-world networks, like social networks or the internet infrastructure, have structural properties such as their large clustering coefficient that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models, such as hyperbolic random graphs. With this paper we contribute to the theoretical analysis of these modern, more realistic random graph models. However, we do not directly study hyperbolic random graphs, but replace them by a more general model that we call \emph{geometric inhomogeneous random graphs} (GIRGs). Since we ignore constant factors in the edge probabilities, our model is technically simpler (specifically, we avoid hyperbolic cosines), while preserving the qualitative behaviour of hyperbolic random graphs, and we suggest to replace hyperbolic random graphs by our new model in future theoretical studies. We prove the following fundamental structural and algorithmic results on GIRGs. (1) We provide a sampling algorithm that generates a random graph from our model in expected linear time, improving the best-known sampling algorithm for hyperbolic random graphs by a factor $O(\sqrt{n})$, (2) we establish that GIRGs have a constant clustering coefficient, (3) we show that GIRGs have small separators, i.e., it suffices to delete a sublinear number of edges to break the giant component into two large pieces, and (4) we show how to compress GIRGs using an expected linear number of bits

Average Distance in a General Class of Scale-Free Networks with Underlying Geometry

In Chung-Lu random graphs, a classic model for real-world networks, each vertex is equipped with a weight drawn from a power-law distribution (for which we fix an exponent $2 < \beta < 3$), and two vertices form an edge independently with probability proportional to the product of their weights. Modern, more realistic variants of this model also equip each vertex with a random position in a specific underlying geometry, which is typically Euclidean, such as the unit square, circle, or torus. The edge probability of two vertices then depends, say, inversely polynomial on their distance. We show that specific choices, such as the underlying geometry being Euclidean or the dependence on the distance being inversely polynomial, do not significantly influence the average distance, by studying a generic augmented version of Chung-Lu random graphs. Specifically, we analyze a model where the edge probability of two vertices can depend arbitrarily on their positions, as long as the marginal probability of forming an edge (for two vertices with fixed weights, one fixed position, and one random position) is as in Chung-Lu random graphs, i.e., proportional to the product of their weights. The resulting class contains Chung-Lu random graphs, hyperbolic random graphs, and geometric inhomogeneous random graphs as special cases. Our main result is that this general model has the same average distance as Chung-Lu random graphs, up to a factor $1+o(1)$. The proof also yields that our model has a giant component and polylogarithmic diameter with high probability

How willing/unwilling are luxury hotels' staff to be empowered? A case of East Malaysia

Empowerment is widely viewed as a dynamic concept to improve service quality and operational efficiency in the hospitality industry. The most effective approaches to empowering employees are not always clear. This paper contributes to the literature by seeking to understand the underlying factors that motivate and demotivate employees' willingness to become empowered. Qualitative data was collected through 22 semi-structured interviews with managers, supervisors and employees of four and five-star rated hotels in East Malaysia. In addition to the expected factors such as employees' acquired knowledge and psychological empowerment, employees' values and beliefs were also found to influence their willingness to become empowered. These findings are important in understanding employee perspectives of empowerment practices in operations contexts of East Malaysian luxury hotels

Foreign Market Re-entry: A Review and Future Research Directions

Foreign market re-entry has increasingly attracted academic interest. However, different streams of research have developed largely independently of each other, which has hindered theory development and practical advancement in the field. By reviewing 45 relevant articles in international business and related disciplines between 1996 and 2020, this study provides a systematic review and analysis of the literature on re-entry. In addition, a framework is developed to direct future research efforts. Following the logic of ‘Antecedents-Phenomenon-Consequences’ and focusing on the time dimension, this study enables better understanding of the re-entry phenomenon and provides recommendations for future research in this area

Greedy Routing and the Algorithmic Small-World Phenomenom

The algorithmic small-world phenomenon, empirically established by Milgram's letter forwarding experiments from the 60s, was theoretically explained by Kleinberg in 2000. However, from today's perspective his model has several severe shortcomings that limit the applicability to real-world networks. In order to give a more convincing explanation of the algorithmic small-world phenomenon, we study greedy routing in a more realistic random graph model (geometric inhomogeneous random graphs), which overcomes the previous shortcomings. Apart from exhibiting good properties in theory, it has also been extensively experimentally validated that this model reasonably captures real-world networks. In this model, we show that greedy routing succeeds with constant probability, and in case of success almost surely finds a path that is an almost shortest path. Our results are robust to changes in the model parameters and the routing objective. Moreover, since constant success probability is too low for technical applications, we study natural local patching methods augmenting greedy routing by backtracking and we show that such methods can ensure success probability 1 in a number of steps that is close to the shortest path length. These results also address the question of Krioukov et al. whether there are efficient local routing protocols for the internet graph. There were promising experimental studies, but the question remained unsolved theoretically. Our results give for the first time a rigorous and analytical answer, assuming our random graph model