Reinforcement learning based local search for grouping problems: A case study on graph coloring

Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search. The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied. Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms

Evolving Pacing Strategies for Team Pursuit Track Cycling

Team pursuit track cycling is a bicycle racing sport held on velodromes and is part of the Summer Olympics. It involves the use of strategies to minimize the overall time that a team of cyclists needs to complete a race. We present an optimisation framework for team pursuit track cycling and show how to evolve strategies using metaheuristics for this interesting real-world problem. Our experimental results show that these heuristics lead to significantly better strategies than state-of-art strategies that are currently used by teams of cyclists

Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets

Canonical tree-decompositions of finite graphs I. Existence and algorithms

We construct tree-decompositions of graphs that distinguish all their k-blocks and tangles of order k, for any fixed integer k. We describe a family of algorithms to construct such decompositions, seeking to maximize their diversity subject to the requirement that they commute with graph isomorphisms. In particular, all the decompositions constructed are invariant under the automorphisms of the graph.Comment: 23 pages, 5 figure

Bridging the Gap Between the Least and the Most Influential Twitter Users

Social networks play an increasingly important role in shaping the behaviour of users of the Web. Conceivably Twitter stands out from the others, not only for the platform's simplicity but also for the great influence that the messages sent over the network can have. The impact of such messages determines the influence of a Twitter user and is what tools such as Klout, PeerIndex or TwitterGrader aim to calculate. Reducing all the factors that make a person influential into a single number is not an easy task, and the effort involved could become useless if the Twitter users do not know how to improve it. In this paper we identify what specific actions should be carried out for a Twitterer to increase their influence in each of above-mentioned tools applying, for this purpose, data mining techniques based on classification and regression algorithms to the information collected from a set of Twitter users.This work has been partially founded by the European Commission Project ”SiSOB: An Observatorium for Science in Society based in Social Models” (http://sisob.lcc.uma.es) (Contract no.: FP7 266588), ”Sistemas Inalámbricos de Gestión de Información Crítica” (with code number TIN2011-23795 and granted by the MEC, Spain) and ”3DTUTOR: Sistema Interoperable de Asistencia y Tutoría Virtual e Inteligente 3D” (with code number IPT-2011-0889- 900000 and granted by the MINECO, Spain