EVOLVE: A Visualization Tool for Multi-objective Optimization Featuring Linked View of Explanatory Variables and Objective Functions

Abstract

Abstract-Multi-objective optimization tools have been applied in various academic and industry fields. It is often difficult to optimize all the objectives since they often cause trade-offs. It is also difficult to figure what kinds of trade-offs actually cause. We think visualization of multi-objective optimization results assists users to intuitively understand the distributions of their solutions. This paper proposes a visualization of explanatory variable and objective function spaces in the separate views, so that users can easily understand their relevancy. Also, our tool features the linkage mechanism between the two views. When a user selects certain ranges of the values by a mouse click operation, the tool highlights all the corresponding individuals. This mechanism is useful for users to narrow down the results. We expect the tool assists the understanding of the behavior of the optimization processes and improvement of the future processes. (Abstract)

Explanatory Journeys: Visualising to Understand and Explain Administrative Justice Paths of Redress

“…Explanations have been used as part of interactive tools to explain different concepts. For instance, in multiple view systems it can be difficult to understand what views are linked [46], which can be explained with overlaid metaviews [30,67]. Similarly variable sensitivity information [58] or trails of user activity could be visualised to explain where users could or have navigated, allowing them to return to previous versions [6].…”

moGrams: A Network-Based Methodology for Visualizing the Set of Nondominated Solutions in Multiobjective Optimization

“…There are some proposals providing a visualization of both objective and decision spaces in separate windows such as cloud visualization [16] and synchronous visualization [35]. The visualization frameworks, VIDEO [12] and EVOLVE [37], followed the same idea. They used standard techniques such as spatial coordinate axes, color, Kriging mapping in VIDEO; and scatterplot with Parallel Coordinate Plot in EVOLVE.…”

“…Visualization method Supplementary algorithms Joint Relations between Network-based visualization solutions in decision space visualization Eddy and Lewis [16] Cloud visualization -Winer and Bloebaum [32] Graph morphing -Agrawal et al [17], [33] HSDC -Obayashi et al [14], [34] SOM ANOVA Pryke et al [15] Heatmap Hierarchical clustering Jeong et al [14], [35] Synchronous visualization -Kollat and Reed [12] Spatial coordinates, size, shape, color, etc. -Blasco et al [36] Level diagrams Data mining classification Walker et al [9] Heatmap Spectral seriation Kubota et al [37] Scatterplot and parallel coordinate plot -Among others, SNA techniques are especially useful to solve two tasks: 1) Network scaling: Networks are usually dense and scaling is necessary to obtain structures revealing the underlying organization, maintaining all the nodes but only the most important relations. Three predominant SNA alternatives to accomplish this task are presented in the literature [40]:…”