Results 91 to 100 of about 40,532 (179)

The power of next-generation-sequencing: A game-changer with limitations. [PDF]

J Eur Acad Dermatol Venereol
Klausegger A, Bauer JW.
europepmc   +1 more source

Artificial intelligence in thoracic surgery: Perspectives and challenges. [PDF]

Surg Pract Sci
Liao W, Peng B, Ding G, Li Z, Wang G.
europepmc   +1 more source

CANA v1.0.0: efficient quantification of canalization in automata networks. [PDF]

Bioinformatics
Marcus AM, Rozum J, Sizek H, Rocha LM.
europepmc   +1 more source

Maximising the Potential of Temporal N e Estimation in Long-Term Population Monitoring Programmes. [PDF]

Mol Ecol Resour
Hui TJ.
europepmc   +1 more source

New Codification Schemas for Scheduling with Genetic Algorithms

, 2005
Codification is a very important issue when a Genetic Algorithm is designed to dealing with a combinatorial problem. In this paper we introduce new codification schemas for the Job Shop Scheduling problem which are extensions of two schemas of common use, and are worked out from the concept of underlying probabilistic model.
Ramiro Varela, David P. Serrano, María R. Sierra   +2 more
openalex   +2 more sources

Problem-Independent Schema Synthesis for Genetic Algorithms

, 2003
As a preprocessing for genetic algorithms, static reordering helps genetic algorithms effectively create and preserve high-quality schemata, and consequently improves the performance of genetic algorithms. In this paper, we propose a static reordering method independent of problem-specific knowledge.
Yong-Hyuk Kim, Yung‐Keun Kwon, Byung-Ro Moon   +2 more
openalex   +2 more sources

Schema theorem of real-coded nonlinear genetic algorithm

Proceedings. International Conference on Machine Learning and Cybernetics, 2003
Through the mechanism analysis of simple genetic algorithm (SGA), every genetic operator can be considered as a linear function. So some disadvantages of SGA may be solved if the genetic operators are modified to a nonlinear function. According to the above method, a nonlinear genetic algorithm is introduced.
Zhihua Cui, Jianchao Zeng
openalex   +2 more sources

Parallel Genetic Algorithms with Schema Migration

Proceedings 26th Annual International Computer Software and Applications, 2003
Genetic algorithms (GAs) are efficient non-gradient stochastic search methods. Parallel GAs are proposed to overcome the deficiencies of sequential GAs, such as low speed and aptness to locally converge. However the tremendous communication cost incurred offsets the advantages of parallel GAs.
Guan Yu
  +4 more sources