Operator and parameter adaptation in genetic algorithms

Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of “Natural Selection”. These algorithms maintaina finite memory of individual points on the search landscape known as the “population”. Members of the population are usually represented as strings written over some fixed alphabet, each of which has a scalar value attached to it reflecting its quality or “fitness”. Thesearch may be seen as the iterative application of a number of operators, such as selection, recombination and mutation, to the population with the aim of producing progressively fitter individuals. These operators are usually static, that is to say that their mechanisms, parameters, and probability of application are fixed at the beginning and constant throughout the run of thealgorithm. However there is an increasing body of evidence that not only is there no single choice of operators which is optimal for all problems, but that in fact the optimal choice of operators for a given problem will be time-variant i.e. it will depend on such factors as thedegree of convergence of the population. Based on theoretical and practical approaches, a number of authors have proposed methods of adaptively controlling one or more of the operators, usually invoking some kind of “meta-learning” algorithm, in order to try and improvethe performance of the Genetic Algorithm as a function optimiser.In this paper we describe the background to these approaches, and suggest a framework for their classification based on the learning strategy used to control them, and what facets of the algorithm are susceptible to adaptation. We then review a number of significant pieces of work within this context, and draw some conclusions about the relative merits of variousapproaches and promising directions for future work

Birefringence upper limit analysis of low birefringence fibers employed in the Faraday effect current sensors

The theoretical model of the Faraday rotation in the low birefringence optical fiber is proposed to serve as a convenient tool for the determination of the birefringence upper limit allowed to retain current sensor sensitivity. The measurement technique offers a fast and efficient determination of the ultra-low linear birefringence when other techniques are not sensitive enough or they are difficult to implement. A temperature dependence of the Faraday rotation and its causes are investigated

Radiative charge transfer lifetime of the excited state of (NaCa)+^+

New experiments were proposed recently to investigate the regime of cold atomic and molecular ion-atom collision processes in a special hybrid neutral-atom--ion trap under high vacuum conditions. The collisional cooling of laser pre-cooled Ca$^+$ ions by ultracold Na atoms is being studied. Modeling this process requires knowledge of the radiative lifetime of the excited singlet A$^1\Sigma^+$ state of the (NaCa)$^+$ molecular system. We calculate the rate coefficient for radiative charge transfer using a semiclassical approach. The dipole radial matrix elements between the ground and the excited states, and the potential curves were calculated using Complete Active Space Self-Consistent field and M\"oller-Plesset second order perturbation theory (CASSCF/MP2) with an extended Gaussian basis, 6-311+G(3df). The semiclassical charge transfer rate coefficient was averaged over a thermal Maxwellian distribution. In addition we also present elastic collision cross sections and the spin-exchange cross section. The rate coefficient for charge transfer was found to be $2.3\times 10^{-16}$ cm$^3$/sec, while those for the elastic and spin-exchange cross sections were found to be several orders of magnitude higher ($1.1\times 10^{-8}$ cm$^3$/sec and $2.3\times 10^{-9}$ cm$^3$/sec, respectively). This confirms our assumption that the milli-Kelvin regime of collisional cooling of calcium ions by sodium atoms is favorable with the respect to low loss of calcium ions due to the charge transfer.Comment: 4 pages, 5 figures; v.2 - conceptual change

Sedimentation and subsidence history of the Lomonosov Ridge

During the first scientific ocean drilling expedition to the Arctic Ocean (Arctic Coring Expedition [ACEX]; Integrated Ocean Drilling Program Expedition 302), four sites were drilled and cored atop the central part of the Lomonosov Ridge in the Arctic Ocean at ~88°N, 140°E (see Fig. F18 in the "Sites M0001–M0004" chapter). The ridge was rifted from the Eurasian continental margin at ~57 Ma (Fig. F1) (Jokat et al., 1992, 1995). Since the rifting event and the concurrent tilting and erosion of this sliver of the outer continental margin, the Lomonosov Ridge subsided while hemipelagic and pelagic sediments were deposited above the angular rifting unconformity (see Fig. F7A in the "Sites M0001–M0004" chapter).The sections recovered from the four sites drilled during Expedition 302 can be correlated using their seismic signature, physical properties (porosity, magnetic susceptibility, resistivity, and P-wave velocity), chemostratigraphy (ammonia content of pore waters), lithostratigraphy, and biostratigraphy. The lithostratigraphy of the composite section combined with biostratigraphy provides an insight into the complex history of deposition, erosion, and preservation of the biogenic fraction. Eventually, the ridge subsided to its present water depth as it drifted from the Eurasian margin. In this chapter, we compare a simple model of subsidence history with the sedimentary record recovered from atop the ridge

Affective Computing for Late-Life Mood and Cognitive Disorders

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer\u27s disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed

The Texas A&M Sheep and Goat Simulation Models

144 p

Implosion hydrodynamics of fast ignition targets

Copyright 2005 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Physics of Plasmas, 12(5), 056312, 2005 and may be found at http://dx.doi.org/10.1063/1.189695

Cosmic Censorship, Area Theorem, and Self-Energy of Particles

The (zeroth-order) energy of a particle in the background of a black hole is given by Carter's integrals. However, exact calculations of a particle's {\it self-energy} (first-order corrections) are still beyond our present reach in many situations. In this paper we use Hawking's area theorem in order to derive bounds on the self-energy of a particle in the vicinity of a black hole. Furthermore, we show that self-energy corrections {\it must} be taken into account in order to guarantee the validity of Penrose cosmic censorship conjecture.Comment: 11 page