Atjaunināt sīkdatņu piekrišanu

E-grāmata: Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013, Proceedings

Edited by , Edited by
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 47,58 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013, ProceedingsPalielināt
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

This book constitutes the refereed proceedings of the 13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoBIO, EvoMUSART, and EvoApplications. The 23 revised full papers presented were carefully reviewed and selected from 50 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economic, and scientific domains. Prominent examples of metaheuristics are ant colony optimization, evolutionary algorithms, greedy randomized adaptive search procedures, iterated local search, simulated annealing, tabu search, and variable neighborhood search. Applications include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman problem, packing and cutting, satisfiability, and general mixed integer programming.
A Hyper-heuristic with a Round Robin Neighbourhood.- A Multiobjective
Approach Based on the Law of Gravity and Mass Interactions for Optimizing
Networks.- A Multi-objective Feature Selection Approach Based on Binary PSO
and Rough Set Theory.- A New Crossover for Solving Constraint Satisfaction
Problems.- A Population-Based Strategic Oscillation Algorithm for Linear
Ordering Problem with Cumulative Costs.- A Study of Adaptive Perturbation
Strategy for Iterated Local Search.- Adaptive MOEA/D for QoS-Based Web
Service Composition.- An Analysis of Local Search for the Bi-objective
Bidimensional Knapsack Problem.- An Artificial Immune System Based Approach
for Solving the Nurse Re-rostering Problem.- Automatic Algorithm Selection
for the Quadratic Assignment Problem Using Fitness Landscape Analysis.-
Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach.-
Combinatorial Neighborhood Topology Particle Swarm Optimization Algorithm for
the Vehicle Routing Problem.- Dynamic Evolutionary Membrane Algorithm in
Dynamic Environments.- From Sequential to Parallel Local Search for SAT.-
Generalizing Hyper-heuristics via Apprenticeship Learning.- High-Order
Sequence Entropies for Measuring Population Diversity in the Traveling
Salesman Problem.- Investigating Monte-Carlo Methods on the Weak Schur
Problem.- Multi-objective AI Planning: Comparing Aggregation and Pareto
Approaches.- Predicting Genetic Algorithm Performance on the Vehicle Routing
Problem Using Information Theoretic Landscape.- Single Line Train Scheduling
with ACO.- Solving Clique Covering in Very Large Sparse Random Graphs by a
Technique Based on k-Fixed Coloring Tabu Search.- Solving the Virtual Network
Mapping Problem with Construction Heuristics, Local Search and Variable
Neighborhood Descent.- The Generate-and-Solve Framework Revisited: Generating
by Simulated Annealing.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97836423719812e.html
Keywords: