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Numerical and Evolutionary Optimization

dc.contributor.authorLara, Adriana*
dc.contributor.authorQuiroz, Marcela*
dc.contributor.authorSchütze, Oliver*
dc.contributor.authorMezura-Montes, Efrén*
dc.date.accessioned2021-02-11T21:19:43Z
dc.date.available2021-02-11T21:19:43Z
dc.date.issued2019*
dc.date.submitted2019-12-09 11:49:16*
dc.identifier42655*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/54914
dc.description.abstractThis book was established after the 6th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.*
dc.languageEnglish*
dc.subjectTA1-2040*
dc.subjectT1-995*
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technologyen_US
dc.subject.othermodel predictive control*
dc.subject.otherbulbous bow*
dc.subject.otherimprovement differential evolution algorithm*
dc.subject.otherevolutionary multi-objective optimization*
dc.subject.otherlocation routing problem*
dc.subject.otherflexible job shop scheduling problem*
dc.subject.otherbasic differential evolution algorithm*
dc.subject.othermetric measure spaces*
dc.subject.otherNEAT*
dc.subject.othergenetic algorithm*
dc.subject.othermultiobjective optimization*
dc.subject.otherimproved differential evolution algorithm*
dc.subject.otherperformance indicator*
dc.subject.otherrubber*
dc.subject.otheraveraged Hausdorff distance*
dc.subject.othermixture experiments*
dc.subject.otherU-shaped assembly line balancing*
dc.subject.otherGenetic Programming*
dc.subject.otherLocal Search*
dc.subject.otherdriving events*
dc.subject.othersurrogate-based optimization*
dc.subject.othersingle component constraints*
dc.subject.othercrop planning*
dc.subject.otherPareto front*
dc.subject.othernumerical simulations*
dc.subject.othershape morphing*
dc.subject.othergenetic programming*
dc.subject.othereconomic crops*
dc.subject.otherlocal search and jump search*
dc.subject.othermodel order reduction*
dc.subject.otheroptimal solutions*
dc.subject.otherEvoSpace*
dc.subject.otherrisky driving*
dc.subject.otherintelligent transportation systems*
dc.subject.otheroptimal control*
dc.subject.otherIV-optimality criterion*
dc.subject.otherBloat*
dc.subject.otherdecision space diversity*
dc.subject.othermodify differential evolution algorithm*
dc.subject.otherpower means*
dc.subject.otherdriving scoring functions*
dc.subject.otheropen-source framework*
dc.subject.otherevolutionary computation*
dc.subject.otherdifferential evolution algorithm*
dc.subject.othervehicle routing problem*
dc.subject.othermulti-objective optimization*
dc.titleNumerical and Evolutionary Optimization*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03921-817-2*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
oapen.relation.isbn9783039218172*
oapen.relation.isbn9783039218165*
oapen.pages230*
oapen.edition1st*


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