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Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Februar 2005
Genre:	Schulbücher
	Algorithm Analysis and Problem Complexity / Algorithms / Artificial Intelligence / C / computer science / genetic algorithms / Heuristics / Künstliche Intelligenz / Mathematical theory of computation / multi-objective optimization / Numeric Computing / Numerical analysis / Numerische Mathematik / Optimization / pareto optimal solutions / Programming / Scheduling / Uncertainty mehr...
ISBN:	9783540249832
EAN-Code:	9783540249832
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Serie:	#3410 - Lecture Notes in Computer Science Theoretical Computer Science and General Issues
Dimensionen:	H 235 mm / B 155 mm / D
Gewicht:	1384 gr
Seiten:	912
Illustration:	XVI, 912 p.
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
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Inhalt:

Multicriterion optimization refers to problems with two or more objectives (n- mally in con?ict with each other) which must be simultaneously satis?ed. Multicriterion optimization problems have not one but a set of solutions (which represent trade-o?s among the objectives), which are called Pareto optimal - lutions. Thus, the main goal in multicriterion optimization is to ?nd or to - proximate the set of Pareto optimal solutions. Evolutionary algorithms have been used for solving multicriterion optimization problems for over two decades, gaining an increasing popularity over the last 10 years. The 3rd International Conference on Evolutionary Multi-criterion Optimi- tion(EMO2005)washeldduringMarch9-11,2005,inGuanajuato,M¿ exico.This wasthethirdinternationalconferencededicatedentirelytothisimportanttopic, followingthesuccessfulEMO2001andEMO2003conferences,whichwereheldin Z¿ urich,SwitzerlandinMarch2001,andinFaro,PortugalinApril2003,respectively. The EMO 2005 scienti?c program included two keynote addresses, one given by Peter Fleming on an engineering design perspective of many-objective op- mization, and the other given by Milan Zeleny on the evolution of optimality. In addition, three tutorials were presented, one on metaheuristics for multiobj- tivecombinatorialoptimizationbyXavierGandibleux,anotheronmultiobjective evolutionary algorithms by Gary B. Lamont, and a third one on performance assessment of multiobjective evolutionary algorithms by Joshua D. Knowles.