Open AccessProceedings Article
Genetic Algorithms for Task Scheduling Problem.
Fatma A. Omara,Mona M. Arafa +1 more
- 01 Jan 2009
pp 479-507
258
TL;DR: In this paper, two genetic algorithms have been developed and implemented, which are genetic algorithms with some heuristic principles that have been added to improve the performance according to the first developed genetic algorithm, two fitness functions have been applied one after the other.
read more
Abstract: The scheduling and mapping of the precedence-constrained task graph to processors is considered to be the most crucial NP-complete problem in parallel and distributed computing systems Several genetic algorithms have been developed to solve this problem A common feature in most of them has been the use of chromosomal representation for a schedule However, these algorithms are monolithic, as they attempt to scan the entire solution space without considering how to reduce the complexity of the optimization process In this paper, two genetic algorithms have been developed and implemented Our developed algorithms are genetic algorithms with some heuristic principles that have been added to improve the performance According to the first developed genetic algorithm, two fitness functions have been applied one after the other The first fitness function is concerned with minimizing the total execution time (schedule length), and the second one is concerned with the load balance satisfaction The second developed genetic algorithm is based on a task duplication technique to overcome the communication overhead Our proposed algorithms have been implemented and evaluated using benchmarks According to the evolved results, it has been found that our algorithms always outperform the traditional algorithms
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Swarm and Evolutionary Computation
Leszek Rutkowski,Marcin Korytkowski,Rafał Scherer,Ryszard Tadeusiewicz,Lotfi A. Zadeh,Jacek M. Zurada +5 more
- 01 Jan 2012
TL;DR: This paper presents a work inspired by the Pachycondyla apicalis ants behavior for the clustering problem, which combines API with the ability of ants to sort and cluster, and introduces new concepts to ant-based models.
349
Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm
Parmeet Kaur,Shikha Mehta +1 more
TL;DR: An augmented Shuffled Frog Leaping Algorithm (ASFLA) based technique for resource provisioning and workflow scheduling in the Infrastructure as a service (IaaS) cloud environment is presented and outperforms Particle Swarm Optimization and SFLA.
131
An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems
TL;DR: A genetic-based algorithm as a meta-heuristic method to address static task scheduling for processors in heterogeneous computing systems and improves the performance of genetic algorithm through significant changes in its genetic functions and introduction of new operators that guarantee sample variety and consistent coverage of the whole space.
119
A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system
Yun Wen,Hua Xu,Jiadong Yang +2 more
TL;DR: A heuristic-based hybrid genetic-variable neighborhood search algorithm is proposed for the minimization of makespan in the heterogeneous multiprocessor scheduling problem, and significantly outperforms several related algorithms in terms of the schedule quality.
117
HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems
TL;DR: A hybrid heuristic method (HSGA) is proposed to find a suitable scheduling for workflow graph, based on genetic algorithm in order to obtain the response quickly moreover optimizes makespan, load balancing on resources and speedup ratio.
103
References
•Book
Adaptation in natural and artificial systems
John H. Holland
- 01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
•Book
Introduction to Algorithms
Thomas H. Cormen,Charles E. Leiserson,Ronald L. Rivest +2 more
- 01 Jan 1990
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
24.8K
Introduction to algorithms: 4. Turtle graphics
TL;DR: In this article, a language similar to logo is used to draw geometric pictures using this language and programs are developed to draw geometrical pictures using it, which is similar to the one we use in this paper.
15.4K
Adaptive probabilities of crossover and mutation in genetic algorithms
M. Srinivas,Lalit M. Patnaik +1 more
- 01 Apr 1994
TL;DR: An efficient approach for multimodal function optimization using genetic algorithms (GAs) and the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA are described.
Evolutionary computation: comments on the history and current state
TL;DR: The purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA), evolution strategies (ES), and evolutionary programming (EP) are described by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism).