Interactive multi-participant tour allocation

Funes, Pablo; Bonabeau, Eric; Herve, J.-Y.; Morieux, Yves

doi:10.1109/cec.2004.1331100

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…Moreover, Funes et al first considered interactive multi-participant tour allocation [25]. They applied interactive evolutionary computation (IEC) to allocate tours for mailmen but also by combining every participant's subjective evaluations.…”

Section: B C*iga and The Social Comparison Theorymentioning

confidence: 98%

A Mobile Tourism Application Model Based on Collective Interactive Genetic Algorithms

Chi

Chu

et al. 2009

2009 Fourth International Conference on Computer Sciences and Convergence Information Technology

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Section: B C*iga and The Social Comparison Theorymentioning

confidence: 98%

A Mobile Tourism Application Model Based on Collective Interactive Genetic Algorithms

Chi

Chu

et al. 2009

2009 Fourth International Conference on Computer Sciences and Convergence Information Technology

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“…The smaller the value, the lower is its total cost. The upper and the lower limits assigned by the user were chosen from the integer set whose elements are in the range of [0, 100], and the upper Table 1 Users' interval Preferences on f 1 User w 1 User w 1 User w 1 User w 1 limit of an evolutionary individual is not smaller than its lower one. The more satisfied the user is with an evolutionary individual, the larger are the upper and the lower limits; (4) command buttons for a population evolving, e.g., "Initialize", "Next Generation", and "End".…”

Section: Human-computer Interfacementioning

confidence: 99%

“…Implicit objectives, however, can only be described by human languages, opinions or thoughts, and these descriptions vary from person to person, time to time and place to place. These problems are referred to as hybrid multi-objective optimization problems (MOPs) [1]. Taking the problem of fashion design as an example, the coordination of colors in different parts is an explicit objective.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithms with user’s preferences for solving hybrid interval multi-objective optimization problems

Gong

Liu

et al. 2015

Appl Intell

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Hybrid interval multi-objective optimization problems are common in real-world applications. These problems involve both explicit and implicit objectives, and the values of these objectives are intervals. Few previous methods are suitable for them. An evolutionary algorithm with a large population and a user's interval preferences was presented to effectively solve the problems in this paper. In the proposed algorithm, a similarity-based strategy was employed to estimate the interval values of implicit objectives of evolutionary individuals that the user had not evaluated in order to alleviate user fatigue; the user's preferences to different objectives were expressed precisely as intervals by solving an auxiliary optimization problem; a sorting scheme based on the user's preferences was proposed to guide the population evolving toward the user's preferred regions. We applied the proposed method to an interior layout problem, which is a typical optimization problem with both interval parameters in the explicit objective and interval value of the implicit objective. The proposed algorithm was compared with four other optimization algorithms on the interior layout problem. Experimental results validated its effectiveness and superiority over the compared algorithms in terms of solution quality and the number of user's evaluations.

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“…Implicit indices can only be described through opinions or thoughts of a person which vary from person to person. We refer to their occurrence as problems with hybrid indices [5]. It is difficult to determine the exact values of the parameters in these explicit indices.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithms for optimization problems with uncertainties and hybrid indices

Gong

Qin

Sun

2011

Information Sciences

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Interactive multi-participant tour allocation

Cited by 13 publications

References 12 publications

A Mobile Tourism Application Model Based on Collective Interactive Genetic Algorithms

A Mobile Tourism Application Model Based on Collective Interactive Genetic Algorithms

Evolutionary algorithms with user’s preferences for solving hybrid interval multi-objective optimization problems

Evolutionary algorithms for optimization problems with uncertainties and hybrid indices

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