Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design

Babbar-Sebens, Meghna; Minsker, Barbara

doi:10.1016/j.asoc.2011.08.054

Cited by 61 publications

(23 citation statements)

References 14 publications

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“…This suggests that over half of the population were able to find more options for acceptable design alternative when they were engaged in the search process in collaboration with the underlying interactive genetic algorithm than with the number of desirable designs generated by the noninteractive genetic algorithm. The effectiveness of IGA in finding user‐desired design alternatives has also been reported by other studies that have investigated IGAs for water resources problems [e.g., Babbar‐Sebens and Minsker , ; Singh et al ., ]. This demonstrates the effectiveness (Research Question 1) of such interactive search methods for assisting users during participatory design of conservation plans.…”

Section: Discussionsupporting

confidence: 64%

“…[] to enable communities to engage in online participatory design of plans on spatial allocation of conservation practices on their landscape. The underlying interactive optimization (or, human‐guided search) algorithm in WRESTORE is based on the Interactive Genetic Algorithm with Mixed Initiative Interaction (IGAMII) algorithm, proposed originally by Babbar‐Sebens and Minsker []. Figure is an overview of workflow that users experience when they engage with WRESTORE.…”

Section: Methodsmentioning

confidence: 99%

“…A global decision maker (e.g., an agency) focused on watershed‐scale objective functions (or, objective space) may regard these modified watershed plans as inferior plans in comparison to the original optimized plans, in contrast to local stakeholders who find them more acceptable instead. Other researchers [e.g., Babbar‐Sebens and Minsker , ; Singh et al ., ; Rosenberg and Madani , ; Read et al ., ] have also begun to advocate for advanced search and optimization techniques that enable decision makers to identify solutions that are more acceptable to a community, and perhaps even in the proximity of the most mathematically optimal solutions in objective space.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Piemonti

Babbar-Sebens

Mukhopadhyay

et al. 2017

Water Resources Research

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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.

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Section: Discussionsupporting

confidence: 64%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Piemonti

Babbar-Sebens

Mukhopadhyay

et al. 2017

Water Resources Research

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“…Thiele et al [28] introduced a preference-based interactive algorithm (PBEA) that adapts the fitness evaluation with an achievement scalarizing function to guarantee an accurate approximation of the desired area in Paretooptimal front. The algorithm IGAMII [2] applies fuzzy logic to simulate the human decision maker and relieve the constant interaction during the evolution.…”

Section: Preferences In Moeasmentioning

confidence: 99%

Collective preferences in evolutionary multi-objective optimization

Cinalli

Martí

Sánchez-Pi³

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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This paper reviews suitable techniques of interactive and preference-based evolutionary multi-objective algorithms to achieve feasible solutions in Pareto-optimal front. We discuss about possible advantages of collective environments to aggregate consistent preferences in the optimization process. Decision maker can highlight the regions of Pareto frontier that are more relevant to him and focus the search only on those areas previously selected. In addition, interactive and cooperative genetic algorithms work on refining users' preferences throughout the optimization process to improve the reference point or fitness function. Nevertheless, expressing preferences from a unique or small group of decision makers may raise unilateral choices issues and pour hints in terms of search parameter. Supported by a large group of human interaction, collective intelligence is suggested to enhance multi-objective results and explore a wider variety of answers.

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“…The approach in [1] is an example of such a study (see also references therein), where the DM's preference model based on a fuzzy inference system was trained during the interactive solution process and used for providing additional preference information on behalf of the DM.…”

Section: Introductionmentioning

confidence: 99%

Towards Automatic Testing of Reference Point Based Interactive Methods

Ojalehto

Podkopaev

Miettinen

2016

Lecture Notes in Computer Science

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Abstract. In order to understand strengths and weaknesses of optimization algorithms, it is important to have access to different types of test problems, well defined performance indicators and analysis tools. Such tools are widely available for testing evolutionary multiobjective optimization algorithms. To our knowledge, there do not exist tools for analyzing the performance of interactive multiobjective optimization methods based on the reference point approach to communicating preference information. The main barrier to such tools is the involvement of human decision makers into interactive solution processes, which makes the performance of interactive methods dependent on the performance of humans using them. In this research, we aim towards a testing framework where the human decision maker is replaced with an artificial one and which allows to repetitively test interactive methods in a controlled environment.

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Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design

Cited by 61 publications

References 14 publications

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

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

Collective preferences in evolutionary multi-objective optimization

Towards Automatic Testing of Reference Point Based Interactive Methods

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