Remote Sensing and Problems of the Hydrosphere

A discussion of freshwater and marine systems is presented including areas of the classification of lakes, identification and quantification of major functional groups of phytoplankton, sources and sinks of biochemical factors, and temporal and regional variability of surface features. Atmospheric processes linked to hydrospheric process through the transfer of matter via aerosols and gases are discussed. Particle fluxes to the aquatic environment and global geochemical problems are examined

Remote Sensing and Problems of the Hydrosphere. A Focus for Future Research

The underly problems of water quality which are addressable with remote sensors are considered. The chemical, biological, geological, and physical dynamics of natural ecosystems are examined

Sub-structural Niching in Estimation of Distribution Algorithms

We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation of distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The proposed method consists of three key components: (1) Problem decomposition and sub-structure identification, (2) sub-structure fitness estimation, and (3) sub-structural niche preservation. The sub-structural niching method is compared to restricted tournament selection (RTS)--a niching method used in hierarchical Bayesian optimization algorithm--with special emphasis on sustained preservation of multiple global solutions of a class of boundedly-difficult, additively-separable multimodal problems. The results show that sub-structural niching successfully maintains multiple global optima over large number of generations and does so with significantly less population than RTS. Additionally, the market share of each of the niche is much closer to the expected level in sub-structural niching when compared to RTS

The non-metallic materials sample array

The Non-Metallic Materials Sample Array (MSA) was flown as verification flight instrumentation (VFI) on both Spacelab 1 (SL-1) and Spacelab 2 (SL-2). The basis for materials selection was either previous flight history or probable flight suitability based upon analysis. The observed changes in the optical properties of the exposed materials are, in general, quite minimal; however, this data represents the short exposure of two Space Shuttle missions, and no attempt should be made to extrapolate the long-term exposure. The MSA was in orbit for 10 days at approximately 240 km on SL-1 and for 7 days at approximately 315 km on SL-2. The array was exposed to the solar flux for only a portion of the time in orbit

Crystal structure prediction using the Minima Hopping method

A structure prediction method is presented based on the Minima Hopping method. Optimized moves on the configurational enthalpy surface are performed to escape local minima using variable cell shape molecular dynamics by aligning the initial atomic and cell velocities to low curvature directions of the current minimum. The method is applied to both silicon crystals and binary Lennard-Jones mixtures and the results are compared to previous investigations. It is shown that a high success rate is achieved and a reliable prediction of unknown ground state structures is possible.Comment: 9 pages, 6 figures, novel approach in structure prediction, submitted to the Journal of Chemical Physic

Properties of Nucleon Resonances by means of a Genetic Algorithm

We present an optimization scheme that employs a Genetic Algorithm (GA) to determine the properties of low-lying nucleon excitations within a realistic photo-pion production model based upon an effective Lagrangian. We show that with this modern optimization technique it is possible to reliably assess the parameters of the resonances and the associated error bars as well as to identify weaknesses in the models. To illustrate the problems the optimization process may encounter, we provide results obtained for the nucleon resonances $\Delta$(1230) and $\Delta$(1700). The former can be easily isolated and thus has been studied in depth, while the latter is not as well known experimentally.Comment: 12 pages, 10 figures, 3 tables. Minor correction

Improved sampling of the pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm

Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems. We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort