[go: up one dir, main page]

Knjazew et al., 2000 - Google Patents

Large-scale permutation optimization with the ordering messy genetic algorithm

Knjazew et al., 2000

Document ID
7679320746779462902
Author
Knjazew D
Goldberg D
Publication year
Publication venue
International Conference on Parallel Problem Solving from Nature

External Links

Snippet

This paper presents a scaling analysis of the ordering messy genetic algorithm (OmeGA), a fast messy genetic algorithm that uses random keys to represent solutions. In experiments with hard permutation problems—so-called ordering deceptive problems—it is shown that …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30943Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
    • G06F17/30946Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type indexing structures
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Forrest Genetic algorithms
França et al. A memetic algorithm for the total tardiness single machine scheduling problem
Aste et al. Complex networks on hyperbolic surfaces
Gonçalves et al. A biased random key genetic algorithm for 2D and 3D bin packing problems
Laboudi et al. Comparison of genetic algorithm and quantum genetic algorithm.
Filipović Fine-grained tournament selection operator in genetic algorithms
Mulder et al. Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks
Daley et al. On the complexity of inductive inference
Dwivedi et al. Travelling salesman problem using genetic algorithm
Giannella et al. Communication efficient construction of decision trees over heterogeneously distributed data
Filipović et al. Fine grained tournament selection for the simple plant location problem
Feng et al. Dynamics of pseudoentanglement
Khan Assessing different crossover operators for travelling salesman problem
Knjazew et al. OMEGA—Ordering messy GA: Solving permutation problems with the fast messy genetic algorithm and random keys
Singh et al. Study of variation in TSP using genetic algorithm and its operator comparison
Cavagna et al. Vicsek model by time-interlaced compression: A dynamical computable information density
Knjazew et al. Large-scale permutation optimization with the ordering messy genetic algorithm
Jones et al. Reverse HillclimbingGenetic Algorithms and the Busy Beaver Problem.
CN114913922B (en) DNA sequence assembling method
Thompson Pruning boosted classifiers with a real valued genetic algorithm
Yang et al. Exact synthesis of 3-qubit quantum circuits from non-binary quantum gates using multiple-valued logic and group theory
Von Moll et al. Genetic algorithm approach for UAV persistent visitation problem
Khuri Genetic Algorithms
Mulder et al. Using adaptive resonance theory and local optimization to divide and conquer large scale traveling salesman problems
Knjazew et al. Solving permutation problems with the ordering messy genetic algorithm