Browsing by Author "Perfecto, Cristina"
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Item Dandelion-encoded harmony search heuristics for opportunistic traffic offloading in synthetically modeled mobile networks(Springer Verlag, 2016) Perfecto, Cristina; Bilbao, Miren Nekane; Del Ser, Javier; Ferro, Armando; Salcedo-Sanz, Sancho; Geem, Zong Woo; Kim, Joong Hoon; IAThe high data volumes being managed by and transferred through mobile networks in the last few years are the main rationale for the upsurge of research aimed at finding efficient technical means to offload exceeding traffic to alternative communication infrastructures with higher transmission bandwidths. This idea is solidly buttressed by the proliferation of short-range wireless communication technologies (e.g.mobile devices with multiple radio interfaces), which can be conceived as available opportunistic hotspots to which the operator can reroute exceeding network traffic depending on the contractual clauses of the owner at hand. Furthermore, by offloading to such hotspots a higher effective coverage can be attained by those operators providing both mobile and fixed telecommunication services. In this context, the operator must decide if data generated by its users will be sent over conventional 4G+/4G/3G communication links, or if they will instead be offloaded to nearby opportunistic networks assuming a contractual cost penalty. Mathematically speaking, this problem can be formulated as a spanning tree optimization subject to cost-performance criteria and coverage constraints. This paper will elaborate on the efficient solving of this optimization paradigm by means of the Harmony Search meta-heuristic algorithm and the so-called Dandelion solution encoding, the latter allowing for the use of conventional meta-heuristic operators maximally preserving the locality of tree representations. The manuscript will discuss the obtained simulation results over different synthetically modeled setups of the underlying communication scenario and contractual clauses of the users.Item A harmony search approach for the selective pick-up and delivery problem with delayed drop-off(Springer Verlag, 2016) Del Ser, Javier; Bilbao, Miren Nekane; Perfecto, Cristina; Salcedo-Sanz, Sancho; Geem, Zong Woo; Kim, Joong Hoon; IAIn the last years freight transportation has undergone a sharp increase in the scales of its underlying processes and protocols mainly due to the ever-growing community of users and the increasing number of on-line shopping stores. Furthermore, when dealing with the last stage of the shipping chain an additional component of complexity enters the picture as a result of the fixed availability of the destination of the good to be delivered. As such, business opening hours and daily work schedules often clash with the delivery times programmed by couriers along their routes. In case of conflict, the courier must come to an arrangement with the destination of the package to be delivered or, alternatively, drop it off at a local depot to let the destination pick it up at his/her time convenience. In this context this paper will formulate a variant of the so-called courier problem under economic profitability criteria including the cost penalty derived from the delayed drop-off. In this context, if the courier delivers the package to its intended destination before its associated deadline, he is paid a reward. However, if he misses to deliver in time, the courier may still deliver it at the destination depending on its availability or, alternatively, drop it off at the local depot assuming a certain cost. The manuscript will formulate the mathematical optimization problem that models this logistics process and solve it efficiently by means of the Harmony Search algorithm. A simulation benchmark will be discussed to validate the solutions provided by this meta-heuristic solver and to compare its performance to other algorithmic counterparts.Item Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems(2016) Elola, Andoni; Del Ser, Javier; Bilbao, Miren Nekane; Perfecto, Cristina; Alexandre, Enrique; Salcedo-Sanz, Sancho; IAThe advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very diverse approaches. In this context this work focuses on the automatic construction of features in supervised learning problems, which differs from the conventional selection of features in that new characteristics with enhanced predictive power are inferred from the original dataset. In particular this manuscript proposes a new iterative feature construction approach based on a self-learning meta-heuristic algorithm (Harmony Search) and a solution encoding strategy (correspondingly, Cartesian Genetic Programming) suited to represent combinations of features by means of constant-length solution vectors. The proposed feature construction algorithm, coined as Adaptive Cartesian Harmony Search (ACHS), incorporates modifications that allow exploiting the estimated predictive importance of intermediate solutions and, ultimately, attaining better convergence rate in its iterative learning procedure. The performance of the proposed ACHS scheme is assessed and compared to that rendered by the state of the art in a toy example and three practical use cases from the literature. The excellent performance figures obtained in these problems shed light on the widespread applicability of the proposed scheme to supervised learning with legacy datasets composed by already refined characteristics.Item On the heritability of dandelion-encoded harmony search heuristics for tree optimization problems(IEEE, 2015-09-24) Perfecto, Cristina; Bilbao, Miren Nekane; Del Ser, Javier; Ferro, Armando; IATree based optimization problems stand for those paradigms where solutions can be arranged within a tree-like graph whose nodes represent the optimization variables of the problem at hand and their interconnecting edges topological and/or hierarchical relationships between such variables. In this context, a research line of increasing interest during the last decade focuses on the derivation of intelligent solution encoding strategies capable of 1) capturing all topological constraints of this particular class of graphs; and 2) preserving their connectivity properties when they undergo combination/mutation operations within approximative evolutionary solvers. This manuscript takes a step over the state of the art by shedding light on the heri-tability properties of the Dandelion tree encoding approach under avant-garde stochastically-controlled evolutionary operators. In particular we elaborate on the topological heritability of the so-called Harmony Memory Considering Rate (HMCR) exploitative operator of the Harmony Search algorithm, a population-based meta-heuristic algorithm that has so far shown to outperform other evolutionary schemes in a wide range of optimization scenarios. Results from extensive Monte Carlo simulations are discussed in terms of the preserved structural properties of the newly produced solutions with respect to the initial Dandelion-encoded population.