No abstract

There is increasing trend in the use of multi-objective genetic algorithms (GAs) to estimate parameter sets in the calibration of hydrological models. Multiobjective GAs facilitate the evaluation of several model evaluation objectives, and the examination of massive combinations of parameter sets. Typically, the outcome is a set of several equally-accurate parameter sets which make-up a trade-off surface between the objective functions, usually referred to as Pareto set. The Pareto set is a set of incomparable parameter sets as each solution has unique parameter values in parameter space with competing accuracy in the objective function space. As would be required for decision making purposes, a single parameter set is usually chosen to represent the model calibration procedure. An automated framework for choosing a single solution from such a trade-off surface has not been thoroughly investigated in the model calibration literature. As a result, this study has outlined an automated framework using the distribution of solutions in objective space and parameter space to select solutions with unique properties from an incomparable set of solutions. Our Pareto set was generated 4470 G. Dumedah et al.southern Ontario. Using cluster analysis to evaluate the distribution of solutions in both objective space and parameter space, we developed four auto-selection methods for choosing parameter sets from the trade-off surface to support decision making. Our method generates solutions with unique properties including a representative pathway in parameter space, a basin of attraction (or the center of mass) in objective space, a proximity to the origin in objective space, and a balanced compromise between objective space and parameter space (denoted BCOP). The BCOP method is appealing as it is an equally-weighted compromise for the distribution of solutions in objective space and parameter space. That is, the BCOP solution emphasizes stability in model parameter values and in objective function values-in a way that similarity in parameter space implies similarity in objective space.

This article investigates the of applicability of adding evolvability promoting mechanisms to a genetic algorithm to enhance its ability to handle perpetually novel dynamic environments, especially one that has stationary periods allowing the Genetic Algorithm (GA) to converge on a temporary global optimum. We utilize both biological and evolutionary computation (EC) definitions of evolvability to create two measures: one based on the improvements in fitness; the other based on the amount of genotypic change. These two evolvability measures are used alongside fitness to modify how selection proceeds in the GA. We call this modified GA the Estimation of Evolvability Genetic Algorithm (EEGA). When tested against a regular GA (with random immigrants), the EEGA is able to track the global optimum more closely than the GA during the dynamic period. Unlike most GA extensions, the EEGA works effectively at a lower level of diversity than does the GA, showing that it is the quality of the diverse members in the population and not just the quantity that helps the GA evolve.

Abstract. Gene space, as it is currently formulated, cannot provide a solid basis for investigating the behavior of the GA. We instead propose an approach that takes population effects into account. Starting from a discussion of diversity, we develop a distance measure between populations and thereby a population metric space. We finally argue that one specific parameterization of this measure is particularly appropriate for use with GAs.

This study has applied the Nondominated Sorting Genetic Algorithm II (NSGA-II) in a two-step assimilation procedure to jointly assimilate brightness temperature into a radiative transfer model and soil moisture into a land surface model. The first assimilation procedure generates a time series of soil moisture by assimilating brightness temperature from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) into the Land Parameter Retrieval Model (LPRM). The second procedure generates assimilated soil moisture by assimilating the soil moisture from LPRM into the Canadian Land Surface Scheme (CLASS). Note that the assimilated soil moisture was generated by merging two soil moisture estimates: one from LPRM and the other from the CLASS simulation. The assimilated soil moisture is better than using the soil moisture determined either from the satellite observation or the land surface scheme alone. This method provides improved model state and parameterizations for both LPRM and CLASS with the aim to facilitate real-time forecasts when satellite information becomes available. Application of this framework to the Brightwater Creek watershed in southern Saskatchewan illustrates the utility of the joint assimilation framework to improve a time series of soil moisture estimates. The estimated soil moisture datasets were evaluated over an agricultural site in southern Saskatchewan using in situ monitoring networks. These results demonstrate that soil moisture generated from assimilation of brightness temperature could be improved by incorporating it into a land surface model. A comparison between the assimilated soil moisture and in situ dataset demonstrates an improvement in accuracy and temporal pattern that is accomplished through the assimilation framework.