Persistent Place-Making in Prehistory: the Creation, Maintenance, and Transformation of an Epipalaeolithic Landscape

Most archaeological projects today integrate, at least to some degree, how past people engaged with their surroundings, including both how they strategized resource use, organized technological production, or scheduled movements within a physical environment, as well as how they constructed cosmologies around or created symbolic connections to places in the landscape. However, there are a multitude of ways in which archaeologists approach the creation, maintenance, and transformation of human-landscape interrelationships. This paper explores some of these approaches for reconstructing the Epipalaeolithic (ca. 23,000–11,500 years BP) landscape of Southwest Asia, using macro- and microscale geoarchaeological approaches to examine how everyday practices leave traces of human-landscape interactions in northern and eastern Jordan. The case studies presented here demonstrate that these Epipalaeolithic groups engaged in complex and far-reaching social landscapes. Examination of the Early and Middle Epipalaeolithic (EP) highlights that the notion of “Neolithization” is somewhat misleading as many of the features we use to define this transition were already well-established patterns of behavior by the Neolithic. Instead, these features and practices were enacted within a hunter-gatherer world and worldview

Aerodynamic optimization using a parallel asynchronous evolutionary algorithm controlled by strongly interacting demes

International audienceA parallel asynchronous evolutionary algorithm controlled by strongly interacting demes for single-- and multi--objective optimization problems is proposed. It is suitable for, even non--homogeneous, multiprocessor systems, ensuring maximum exploitation of the available processors. The search algorithm utilizes a structured topology of evaluation agents organized in a number of inter--communicating demes arranged on a 2D supporting mesh. Once an evaluation terminates and a processor becomes idle, a series of intra-- and inter--deme processes determine the next agent to undergo evaluation on this specific processor. Real coding and differential evolution operators are used. Mathematical and aerodynamic--turbomachinery optimization problems are presented to assess the proposed method in terms of CPU cost, parallel efficiency and quality of solutions obtained within a predefined number of evaluations. Comparisons with conventional evolutionary algorithms, parallelized based on the master--slave model on the same computational platform, are presented

Multilevel optimization strategies based on metamodel-assisted evolutionary algorithms, for computationally expensive problems

Abstract — In this paper, three multilevel optimization strate-gies are presented and applied to the design of isolated and cascade airfoils. They are all based on the same general–purpose search platform, which employs Hierarchi-cal, Distributed Metamodel–Assisted Evolutionary Algorithms (HDMAEAs). The core search engine is an Evolutionary Algo-rithm (EA) assisted by local metamodels (radial basis function networks) which, for each population member, are trained anew on a “suitable ” subset of the already evaluated solutions. The hierarchical scheme has a two–level structure, although it may accommodate any number of levels. At each level, the user may link (a) a different evaluation tool, such as low or high fidelity discipline–specific software, (b) a different optimization method, selected amongst stochastic and deterministic algo-rithms and/or (c) a different set of design variables, according to coarse and fine problem parameterizations. In the aerodynamic shape optimization problems presented in this paper, the three aforementioned techniques resort on (a) Navier–Stokes and integral boundary layer solvers, (b) evolutionary and gradient– descent algorithms where the adjoint method computes the objective function gradient and (c) airfoil parameterizations with different numbers of Bézier control points. The EAs used at any level are coarse–grained distributed EAs with a different MAEA at each deme. The three variants of the HDMAEA can be used either separately or in combination, in order to reduce the CPU cost. The optimization software runs in parallel, on multiprocessor systems