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By Richard Balling (auth.), Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Lothar Thiele, Kalyanmoy Deb (eds.)
This booklet constitutes the refereed court cases of the second one overseas convention on Evolutionary Multi-Criterion Optimization, EMO 2003, held in Faro, Portugal, in April 2003.
The fifty six revised complete papers offered have been rigorously reviewed and chosen from a complete of a hundred submissions. The papers are geared up in topical sections on aim dealing with and challenge decomposition, set of rules advancements, on-line model, challenge development, functionality research and comparability, substitute equipment, implementation, and applications.
Read or Download Evolutionary Multi-Criterion Optimization: Second International Conference, EMO 2003, Faro, Portugal, April 8–11, 2003. Proceedings PDF
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Parallel coordinates representation of the data shown in Fig. 2 Conflict, Harmony, and Independence 27 Wegman [19] presents some valuable insights and extensions towards using the parallel coordinates representation as a high-dimensional data analysis tool. Statistical interpretations of the plots are possible, with features such as marginal densities, correlations, clusters, and modes proving readily identifiable. Parallel coordinates plots can suffer from over-plotting for large data sets and thus a density plot variant is also presented in the paper to overcome this.
5 Fig. 5. Scatterplot matrix representation of the data shown in Fig. 2 Quantitative Methods Several pair-wise methods exist for quantifying conflict between criteria that use similar concepts to the parallel coordinates notion of crossing lines. C. J. Fleming dant samples (as one variable increases/decreases, the other follows suit) and the number of discordant samples (as one variable increases/decreases, the other does the opposite) [20]. Thus, discordance produces crossing lines whilst concordance does not.
It is able to detect some exact efficient solutions only when the instance size is less than or equal to 30×30 variables. We underlined the advantage of using good genetic information in a previous paper [2] using the biobjective knapsack problem as an experimental support. Such “good genetic information” can be deduced from the set of supported efficient solutions or from some approximations (the current approximation of the efficient set, or basic approximations obtained with a simple greedy algorithm).