A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks

We consider the problem of optimally designing a body wireless sensor network, while taking into account the uncertainty of data generation of biosensors. Since the related min-max robustness Integer Linear Programming (ILP) problem can be difficult to solve even for state-of-the-art commercial optimization solvers, we propose an original heuristic for its solution. The heuristic combines deterministic and probabilistic variable fixing strategies, guided by the information coming from strengthened linear relaxations of the ILP robust model, and includes a very large neighborhood search for reparation and improvement of generated solutions, formulated as an ILP problem solved exactly. Computational tests on realistic instances show that our heuristic finds solutions of much higher quality than a state-of-the-art solver and than an effective benchmark heuristic.Comment: This is the authors' final version of the paper published in G. Squillero and K. Sim (Eds.): EvoApplications 2017, Part I, LNCS 10199, pp. 1-17, 2017. DOI: 10.1007/978-3-319-55849-3\_16. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-55849-3_1

A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem

Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain. © 2016 Massachusetts Institute of Technolog

Two Kinds of Adaptation, Two Kinds of Relativity

This paper presents a review of adaptation concepts at the evolutionary, environmental, neural, sensory, mental and mathematical levels, including Helson’s and Parducci’s theories of perception and category judgments. Two kinds of adaptation can be clearly distinguished. The first, known as level adaptation, refers to the shift of the neutral perception level to the average stimulus value. It results in a single reference point and stimuli changes represented in absolute terms. This concept is employed by Prospect Theory, which assumes that gains and losses are perceived as monetary amounts. The second kind of adaptation refers to the adjustment of perception sensitivity to stimuli range. It results in two reference points (minimum and maximum stimulus) and stimuli changes perceived in relative terms. Both range adaptation and range relativity are well documented phenomena and have even been confirmed by the creators of Prospect Theory. This makes room for another decision making theory based on the range relativity approach. As shown by Kontek (2009), such a theory would not require the concept of probability weighting to describe lottery experiments or behavioral paradoxes

Interference aware (IA) MAC: an enhancement to IEEE802.11b DCF

The IEEE802.11 has been devised explicitly for low mobility and single access point scenarios, so its effectiveness is impaired in conditions with not negligible interference in ad hoc and/or in infrastructure modes. Well known problems typical of these scenarios are the so called hidden and exposed terminals. In this paper we propose and test a novel MAC layer for wireless LANs, able to improve the performance of IEEE802.11 DCF (distributed coordination function) in environments with high interference levels. The key point is to insert information about received power and interference levels into MAC control packets. By computing an estimation of the interference increasing due to an eventual transmission (forbidden with IEEE802.11 because blocked by the virtual carrier sensing), the number of parallel transmissions can grow significantly