Abstract — Developing a problem-domain independent methodology to automatically generate high performing solving strategies for specific problems is one of the challenging trends on hyper-heuristics design. Designing hyper-heuristics is important because they raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. In this paper, we present a three-steps methodology that combines multiple sequence alignment and grammatical induction in order to automatically generate high performing solving strategies for a combinatorial optimisation problem. We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem. I.

Abstract—This paper introduces a novel method of generating

Abstract—Delay tolerant networks are characterized by the sporadic connectivity between their nodes and therefore the lack of stable end-to-end paths from source to destination. Since the future node connections are mostly unknown in these networks, opportunistic forwarding is used to deliver messages. However, making effective forwarding decisions using only the network characteristics (i.e. average intermeeting time between nodes) extracted from contact history is a challenging problem. Based on the observations about human mobility traces and the findings of previous work, we introduce a new metric called conditional intermeeting time, which computes the average intermeeting time between two nodes relative to a meeting with a third node using only the local knowledge of the past contacts. We then look at the effects of the proposed metric on the shortest path based routing designed for delay tolerant networks. We propose Conditional Shortest Path Routing (CSPR) protocol that routes the messages over conditional shortest paths in which the cost of links between nodes is defined by conditional intermeeting times rather than the conventional intermeeting times. Through trace-driven simulations, we demonstrate that CSPR achieves higher delivery rate and lower end-to-end delay compared to the shortest path based routing protocols that use the conventional intermeeting time as the link metric. I.

Abstract – Identifying the behavioral patterns in a social network setting is beneficial to understand how people behave in certain application domains. Such patterns can also be utilized to characterize social signals such as social roles from interactions. In this work, we examine how probabilistic context free grammars (PCFGs) can be utilized to model interactions and role taking in a social network. We describe how to automatically build a PCFG given a set of interactions as the training data. Our experiments on the Mission Survival Corpus 1 (MSC-1) dataset show that PCFGs are a concise way of modeling social entity behaviors and are useful in understanding the probability distribution of interactions as well as the behavior types that are observed.

Social networks analysis includes examining the actions of entities in a social setting. These actions can be either interactions between entities (e.g. talking, exchanging items etc.), or actions which do not include interactions, but nevertheless are happening in a social context, hence are influenced by social relations. Such actions often contain behavioral patterns that are specific to the actions involved. It is important to understand such patterns to be able to model social environments reliably. In this work, we introduce a novel method for modeling and classifying behavior of nodes in a social network using Probabilistic Context Free Grammars (PCFGs). Informally, PCFGs are regular context free grammars (consisting of START symbol, terminals, nonterminals