By Kaisa Miettinen (auth.), Eckart Zitzler, Lothar Thiele, Kalyanmoy Deb, Carlos Artemio Coello Coello, David Corne (eds.)
This e-book constitutes the refereed lawsuits of the 1st foreign convention on Multi-Criterion Optimization, EMO 2001, held in Zurich, Switzerland in March 2001.
The forty five revised complete papers awarded have been rigorously reviewed and chosen from a complete of 87 submissions. additionally integrated are educational surveys and invited papers. The e-book is prepared in topical sections on set of rules advancements, functionality overview and comparability, constraint dealing with and challenge decomposition, uncertainty and noise, hybrid and substitute tools, scheduling, and purposes of multi-objective optimization in quite a few fields.
Read or Download Evolutionary Multi-Criterion Optimization: First International Conference, EMO 2001 Zurich, Switzerland, March 7–9, 2001 Proceedings PDF
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Extra resources for Evolutionary Multi-Criterion Optimization: First International Conference, EMO 2001 Zurich, Switzerland, March 7–9, 2001 Proceedings
Example text
Figure 4 (taken from Srinivas and Deb [60]) shows the general flow chart of this approach. 3 The use of a ranking scheme based on the concept of Pareto optimality was originally proposed by Goldberg [29]. A Short Tutorial on Evolutionary Multiobjective Optimization 29 START initialize population gen = 0 front = 1 is population classified ? No identify Nondominated individuals Yes reproduction according to dummy fitness gen = gen + 1 Yes assign dummy fitness crossover sharing in current front mutation front = front + 1 is gen < maxgen ?
Our discussion finishes with a short description of two possible areas of future research in EMOO: mechanisms that facilitate the incorporation of user’s preferences and the search for efficient procedures and algorithms for evolutionary multiobjective optimization and to keep diversity. Acknowledgements. The author gratefully acknowledges support from CONACyT through project 34201-A. References 1. T. Arslan, D. H. Horrocks, and E. Ozdemir. Structural Synthesis of Cell-based VLSI Circuits using a Multi-Objective Genetic Algorithm.
Several other similar metrics have been also suggested recently by Zitzler et al. [75]. A. e. the Pareto fronts) produced by the EMOO technique under study, and/or compare it against other similar techniques. There are few comparative studies of EMOO techniques where these metrics have been used and more comprehensive comparisons are still lacking in the literature [75,64,74]. Also, it is important to consider that most of the previously mentioned metrics assume that the user can generate the global Pareto front of the problem under study (using, for example, an enumerative approach), and that will not be possible in most real-world applications.