Dual Sequence Simulated Annealing with Round-Robin Approach for University Course Timetabling

The university course timetabling problem involves assigning a given number of events into a limited number of timeslots and rooms under a given set of constraints; the objective is to satisfy the hard constraints (essential requirements) and minimize the violation of soft constraints (desirable requirements). In this study we employed a Dual-sequence Simulated Annealing (DSA) algorithm as an improvement algorithm. The Round Robin (RR) algorithm is used to control the selection of neighbourhood structures within DSA. The performance of our approach is tested over eleven benchmark datasets. Experimental results show that our approach is able to generate competitive results when compared with other state-of-the-art techniques.

Understanding how animals fight with Lloyd Morgan's canon

Various game theory models have been used to explain animal contests. Here we attend to the presumed cognitive abilities required by these models with respect to information gathering and consequent decision making. Some, such as the hawk/dove game and self-assessment models require very limited cognitive ability. By contrast, the broadly accepted sequential assessment model requires that contestants know their own abilities and compare them with information gathered about their opponent to determine which has the greater resource-holding power. However, evidence for assessment of relative abilities is sparse and we suggest that this complex ability is probably beyond most animals. Indeed, perceptual limitations may restrict information about an individual's own displays and thus preclude comparison. We take a parsimonious view and conclude that simple summation of causal factors accounts for changes in fight motivation without requiring mutual evaluation of relative abilities. © 2012 The Association for the Study of Animal Behaviour.