Exploratory Data Analysis with Interactive Evolution

Malinchik, Sergey; Bonabeau, Eric

doi:10.1007/978-3-540-24855-2_124

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…For example, in interactive evolution, an approach primarily applied to design problems, the computer generates solutions via biologically inspired methods and the user selects which solutions will be used to generate novel solutions in the next iteration (Sims 1991;Todd and Latham 1992;Poli and Cagnoni 1997;Sato 2002;Hammond 2003;Cheng and Kosorukoff 2004;Malinchik et al 2004).…”

Section: Related Workmentioning

confidence: 99%

Human-guided search

et al. 2009

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We present a survey of techniques and results from the Human-Guided Search (HuGS) project, an effort to investigate interactive optimization. HuGS provides simple and general visual metaphors relating to local search operations that allow users to guide the exploration of the search space. These metaphors apply to a wide variety of problems and combinatorial optimization algorithms, which we demonstrate by describing the HuGS toolkit and as well as eight diverse applications we developed using it. User experiments show that human guidance can improve the performance of powerful heuristic search algorithms. HuGS is also a valuable development environment for understanding and improving optimization algorithms. Although HuGS was designed for human-computer interaction, for two different problems we have used the HuGS code base to develop completely automatic heuristic algorithms that produced at the time new best automatic results on benchmark problem instances.

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Section: Related Workmentioning

confidence: 99%

Human-guided search

et al. 2009

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“…However, unlike our work, their method focuses on generating linear projections only, and tries to preserve, as much as possible, a predefined data quality metric. More similar to our method, Malinchik and Bonabeau (2004) use an IEC to perform exploratory data analysis, combining computational search with human evaluation. They show how IEC can be used to evolve two-dimensional linear projections that bring insight about the data.…”

Section: Guided Visual Searchmentioning

confidence: 99%

Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search

Boukhelifa

Bezerianos

Cancino

et al. 2017

Evolutionary Computation

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We evaluate and analyse a framework for evolutionary visual exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactive evolutionary algorithm to steer the exploration of multidimensional data sets toward two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this article, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clearly predefined task, which examines how users leverage the system and how the system evolves to match their needs. While we previously showed that using EVE, domain experts were able to formulate interesting hypotheses and reach new insights when exploring freely, our new findings indicate that users, guided by the interactive evolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating interactive evolutionary computation (IEC) techniques based on user study methodologies.

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“…EDA is a time-domain data visualization tool for exhibiting the data statistical properties; it is adaptive and efficient and needs no prior information [13]. A considerable amount of research on theories and applications of EDA was made [14,15]. EDA based on boxplot and robust-class selection was applied for geochemical mapping in the research of Bounessah, and the boxplot was proved to be very useful in capturing the distribution, skewness, and outliers of the data [16].…”

Section: Introductionmentioning

confidence: 99%

“…EDA based on boxplot and robust-class selection was applied for geochemical mapping in the research of Bounessah, and the boxplot was proved to be very useful in capturing the distribution, skewness, and outliers of the data [16]. A new EDA technique based on interactive evolutionary computation was proposed by Malinchik and Bonabeau, and a rapid data dimension reduction was realized by the proposed technique [15]. A large number of basic examples of applying EDA were introduced by Mast and Trip to demonstrate the general process of EDA [17].…”

Section: Introductionmentioning

confidence: 99%

Data Inspecting and Denoising Method for Data‐Driven Stochastic Subspace Identification

Zhou

Cao

Khan

et al. 2018

Shock and Vibration

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Data-driven stochastic subspace identification (DATA-SSI) is frequently applied to bridge modal parameter identification because of its high stability and accuracy. However, the existence of abnormal data and noise components may make the identification result of DATA-SSI unreliable. In order to achieve a reliable identification result of the bridge modal parameter, a data inspecting and denoising method based on exploratory data analysis (EDA) and morphological filter (MF) was proposed for DATA-SSI. First, EDA was adopted to inspect the data quality for removing the data measured from malfunctioning sensors. Then, MF along with an automated structural element (SE) size determination technique was adopted to suppress the noise components. At last, DATA-SSI and stabilization diagram were applied to identify and exhibit the bridge modal parameter. A model bridge and a real bridge were used to verify the effectiveness of the proposed method. The comparison of the identification results of the original data and improved data was made. The results show that the identification results obtained with the improved data are more accurate, stable, and reliable.

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Exploratory Data Analysis with Interactive Evolution

Cited by 4 publications

References 4 publications

Human-guided search

Human-guided search

Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search

Data Inspecting and Denoising Method for Data‐Driven Stochastic Subspace Identification

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