Interactive genetic algorithm for user‐centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior

Abstract

Interactive Genetic Algorithms (IGA) are advanced human‐in‐the‐loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end‐users. In this paper, we present results and analyses of observational experiments in which different human participants (surrogates and stakeholders) interacted with an IGA‐based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives.

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

“…There are several compelling benefits of involving a human user in the interactive optimization process. First, the incorporation of expert knowledge, intuition and experience can compensate the unavoidable simplifications induced by the model (Meignan et al, 2015;Piemonti et al, 2017a;Liu et al, 2018). Second, computational effort is reduced by focusing on only the most promising regions of the solution space (Balling et al, 1999;do Nascimento and Eades, 2005;Liu et al, 2018).…”

User Preference Adaptive Fitness of Interactive Genetic Algorithm Based Ceramic Disk Pattern Generation Method

“…In order to verify the effectiveness of the method in this paper, our method is compared with three state-of-the-art methods [11], [14], [20]. In our method and other three comparison methods, the population size N is 150, the maximum evolutionary number T 20.…”

“…It is further completed the fitness comparison of methods [11], [14], [20] and our method, and the experimental results are shown in Fig.9-10 and Tab.1-2. The following conclusions can be drawn from the figures and tables.…”

“…The following conclusions can be drawn from the figures and tables. (1) with the progress of the evolutionary process, the median point and width of the average fitness of the population of our method and methods [11], [14], [20] continuously decrease, and the population quality are continuously improved; (2) The user preference model of our method is combined with the individual fitness estimation. Compared with the fitness estimation method which only uses the predicted value, the operation efficiency of evolutionary selection is improved and the probability of finding a satisfactory solution is increased in our method; (3) By adopting our fitness estimation method, the quality of the optimal solution obtained is equivalent to the genetic algorithm based on the improved fitness function.…”

Uncertainty-based Deep Learning Networks for Limited Data Wetland User Models

“…Due to space limitations, it is not possible to fully consider the IGAMII algorithm implementation, WRESTORE system design, or decision maker (DM) approach; however, all of this is more fully described in the cited papers. [8] [9] [10] Previous work in the system has included the training of virtual decision makers or user models which will offer the perspective of different stakeholders involved in the interactive optimization process. Several different approachers for user modeling were implemented and compared, including traditional artificial neural networks, fuzzy logic, and deep neural networks.…”