Human-guided search

Klau, Gunnar W.; Lesh, Neal; Marks, Joe; Mitzenmacher, Michael

doi:10.1007/s10732-009-9107-5

Cited by 64 publications

(46 citation statements)

References 35 publications

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“…It has been demonstrated that interactive approaches to optimization problems are able to surpass mere computer-based solutions (Anderson, et al 2000, Klau et al 2009), since additional knowledge of human experts can be used to further optimize a solution. An advantage of such interactive approaches is that the generated solution is better understandable and more trustworthy to the user if taken into the loop.…”

Section: Related Workmentioning

confidence: 99%

“…While the authors state that a so called Optimization Table allows for several users to interact with the system, no additional information is given on how this type of interaction looks like in an actual application. Lesh et al (2005) extend the HuGS platform to the domain of packing problems and make three types of actions available for human guidance (Klau et al 2009): (1) manual selection of the next move, (2) invocation, monitoring, and halting the search process, and (3) reverting to a previous solution. Kopfer and Schönberger (2002) suggest a framework for interactive problem solving with focus on vehicle routing and scheduling problems in.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Interactive Human-Guided Optimization for Logistics Planning

Mattar¹,

Kulms²,

Kopp³

2015

Mensch Und Computer 2015 - Tagungsband

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Logistics planning is an important problem in industry, where goods have to be parceled appropriately to meet delivery dates or reduce shipping costs. This optimization problem is classically solved offline using standard algorithms and focused heuristics, e.g. bin packing or route planning. However, in practical work environments, constraints may change flexibly and it is often not clear what an optimal solution looks like. Further, logistics planning consists of multiple steps that often are handled by different human employees in different departments. In this paper we propose an interactive approach using human-guided optimization, where solution spaces can be interactively explored, manipulated, and constrained at runtime. Based on an analysis of the problem of multi-step logistics planning, we present a system that supports users in solving this optimization problem, and we report first evaluation results obtained in the first two iterations of a user-centered design process.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Interactive Human-Guided Optimization for Logistics Planning

Mattar¹,

Kulms²,

Kopp³

2015

Mensch Und Computer 2015 - Tagungsband

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“…Researchers have proposed method to apply TS to IEC candidate solutions retrieval [14], [15]. Klau et al have proposed a human-guided TS method covering the Traveling Salesman Problem (TSP) and the Scheduling Problem (SP) as interactive optimization problems [14].…”

Section: Introductionmentioning

confidence: 99%

“…Klau et al have proposed a human-guided TS method covering the Traveling Salesman Problem (TSP) and the Scheduling Problem (SP) as interactive optimization problems [14]. Munemoto et al have proposed an interactive multi-objective optimization method using TS for a floor layout [15].…”

Section: Introductionmentioning

confidence: 99%

Interactive Evolutionary Computation Using a Tabu Search Algorithm

Takenouchi

Tokumaru

Muranaka

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe present an Interactive Tabu Search (ITS) algorithm to reduce the evaluation load of Interactive Evolutionary Computation (IEC) users. Most previous IEC studies used an evaluation interface that required users to provide evaluation values for all candidate solutions. However, user's burden with such an evaluation interface is large. Therefore, we propose ITS where users choose the favorite candidate solution from the presented candidate solutions. Tabu Search (TS) is recognized as an optimization technique. ITS evaluation is simpler than Interactive Genetic Algorithm (IGA) evaluation, in which users provide evaluation values for all candidate solutions. Therefore, ITS is effective for reducing user evaluation load. We evaluated the performance of our proposed ITS and a Normal IGA (NIGA), which is a conventional 10-stage evaluation, using a numerical simulation with an evaluation agent that imitates human preferences (Kansei). In addition, we implemented an ITS evaluation for a runningshoes-design system and examined its effectiveness through an experiment with real users. The simulation results showed that the evolution performance of ITS is better than that of NIGA. In addition, we conducted an evaluation experiment with 21 subjects in their 20 s to assess the effectiveness of these methods. The results showed that the satisfaction levels for the candidates generated by ITS and NIGA were approximately equal. Moreover, it was easier for test subjects to evaluate candidate solutions with ITS than with NIGA. key words: interactive evolutionary computation, user interface, interactive tabu search

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“…In this fashion, the SLS may be redesigned to extend beyond the initial configuration space. Examples of white-box approaches include Statistical Analysis (Fitness Distance Correlation Analysis, Run Time Distribution, etc) [3,8], Sequential Parameter Optimization [9], Human Guided Tabu Search [10], Visualization of Search Process [11,12], V-MDF and Viz [5,[13][14][15], etc.…”

Section: Introductionmentioning

confidence: 99%

Designing and Tuning SLS Through Animation and Graphics: An Extended Walk-Through

Halim

Yap

Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics

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Abstract. Stochastic Local Search (SLS) is quite effective for a variety of Combinatorial (Optimization) Problems (COP). However, the performance of SLS depends on several factors and getting it right is not trivial. In practice, SLS may have to be carefully designed and tuned to give good results. Often this is done in an ad-hoc fashion. One approach to this issue is black-box where a tuning algorithm is used to find good parameters for the black-box SLS. Another approach is white-box which takes advantage of the human in the process. In this paper, we show how visualization using a generic visual tool can be effective for a white-box approach to get the right SLS behavior on the fitness landscape of the problem instances at hand. We illustrate this by means of an extended walk-through on the Quadratic Assignment Problem. At the same time, we present the human-centric tool which have been developed.

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Human-guided search

Cited by 64 publications

References 35 publications

Interactive Human-Guided Optimization for Logistics Planning

Interactive Human-Guided Optimization for Logistics Planning

Interactive Evolutionary Computation Using a Tabu Search Algorithm

Designing and Tuning SLS Through Animation and Graphics: An Extended Walk-Through

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