article · 2013

Heuristics Genetic algorithm

Preeti Singh, Chetan Gupta, Kapil Kashyap, Manav Rachna

Cited by 0Abstract and details only

Abstract

Abstract: Genetic algorithm is very powerful technique to find approximate solution to search problems or patterns through application based on biological terms. Genetic algorithms use biologically inspired techniques such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover. The general strengths of genetic algorithms lie in their ability to explore the search space efficiently through parallel evaluation of fitness and mixing of partial solutions through crossover, maintain a search frontier to seek global optima and solve multi-criterion optimization problems. In this paper, Application based on scheduling static tasks in homogeneous parallel system is discussed. Especially uni-processor system would not be sufficient enough to execute all the tasks parallel so it requires an efficient algorithm to determine when and on which processor a given task should execute A static task can be partitioned into a group of subtasks and represented as a DAG (Directed Acyclic Graph), that problem can be stated as finding a schedule for a DAG to be executed in a parallel multiprocessor system.

Cite this paper

APA 7

Singh, P., Gupta, C., Kashyap, K., & Rachna, M. (2013). Heuristics Genetic algorithm.

MLA 9

Singh, Preeti, et al. “Heuristics Genetic algorithm.” 2013.

Chicago

Singh, Preeti, Chetan Gupta, Kapil Kashyap, and Manav Rachna. 2013. “Heuristics Genetic algorithm.”

Harvard

Singh, P. et al. (2013) ‘Heuristics Genetic algorithm’.

BibTeX

@article{singh2013heuristics,
  title = {{Heuristics Genetic algorithm}},
  author = {Singh, Preeti and Gupta, Chetan and Kashyap, Kapil and Rachna, Manav},
  year = {2013}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.