SOPC-Based Parallel Genetic Algorithm

Evolvable Hardware (EHW) is inspired by natural evolution for the automatic design of hardware systems, based on Evolutionary Algorithm (EA). This paper proposed a novel optimization process of evolution system by utilizing a two-level hierarchical parallel algorithm and constructing EHW system into NoC infrastructure. The NoC is specially designed for high-speed reconfigurable hardware. Experimental results show that the usage of the hierarchical parallel algorithm can achieve 188.7% and 675.7% improvement in convergence speed against the global parallel one or the serial one; moreover, by using the NoC architecture, the Single Evolution Cycle Run Time can be at least two orders of magnitude faster than the state-of-the-art EHW systems when evolving the same scale of circuits.

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“…Selection: selects four parents and one elitism individual to create new offspring by modified tournament selection [18].…”

Section: Random Number Generation (Rng): Generates Pseudo Random Numbmentioning

confidence: 99%

A hierarchical parallel evolvable hardware based on network on chip

Wang

Lai

2013

2013 International Conference on Reconfigurable Computing and FPGAs (ReConFig)

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“…The focus is on a deterministic scheduling problem in which there exist precedence relations among the tasks to be scheduled. A deterministic scheduling problem [16] is one in which all information about the tasks and the relation to each other such as execution time and precedence relation are known to the scheduling algorithm in advance and the processor environment is heterogeneous [23] [24][26] [27]. Heterogeneity of processors means that the processors have different speeds or processing capabilities.…”

Section: Problem Definitionmentioning

confidence: 99%

Improved Task Scheduling on Parallel System using Genetic Algorithm

Singh¹,

Singh²

2012

IJCA

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Parallel Processing refers to the concept of speeding-up the execution of a task by dividing the task into multiple fragments that can execute simultaneously, each on its own processor i.e. it is the simultaneous processing of the task on two or more processors in order to obtain faster results. It can be effectively used for tasks that involve a large number of calculations, have time constraints and can be divided into a number of smaller tasks. The scheduling problem deals with the optimal assignment of a set of tasks onto parallel multiprocessor system and orders their execution so that the total completion time is minimized. The efficient execution of the schedule on parallel multiprocessor system takes the structure of the application and the performance characteristics of the proposed algorithm. Many heuristics and approximation algorithms have been proposed to fulfill the scheduling task. It is well known NP-complete problem. This study proposes a genetic based approach to schedule parallel tasks on heterogeneous parallel multiprocessor system. The scheduling problem considered in this study includesnext to search for an optimal mapping of the task and their sequence of execution and also search for an optimal configuration of the parallel system. An approach for the simultaneous optimization of all these three components of scheduling method using genetic algorithm is presented and its performance is evaluated in comparison with the First Come First Serve (FCFS), Shortest Job First (SJF), Round Robin (RR), Priority and Largest Job First (LJF) scheduling methods.

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“…The parameters of membership functions and consequent part of the network were coded and an improved GA was used to search for the optimum parameters. However, GA is characterized by huge computation time and slow convergence near the optimum [29].…”

Section: Introductionmentioning

confidence: 99%

Variances Handling Method of Clinical Pathways Based on T-S Fuzzy Neural Networks with Novel Hybrid Learning Algorithm

Jiang

Diao

et al. 2010

J Med Syst

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Clinical pathways' variances present complex, fuzzy, uncertain and high-risk characteristics. They could cause complicating diseases or even endanger patients' life if not handled effectively. In order to improve the accuracy and efficiency of variances handling by Takagi-Sugeno (T-S) fuzzy neural networks (FNNs), a new variances handling method for clinical pathways (CPs) is proposed in this study, which is based on T-S FNNs with novel hybrid learning algorithm. And the optimal structure and parameters can be achieved simultaneously by integrating the random cooperative decomposing particle swarm optimization algorithm (RCDPSO) and discrete binary version of PSO (DPSO) algorithm. Finally, a case study on liver poisoning of osteosarcoma preoperative chemotherapy CP is used to validate the proposed method. The result demonstrates that T-S FNNs based on the proposed algorithm achieves superior performances in efficiency, precision, and generalization ability to standard T-S FNNs, Mamdani FNNs and T-S FNNs based on other algorithms (CPSO and PSO) for variances handling of CPs.

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SOPC-Based Parallel Genetic Algorithm

Cited by 16 publications

References 25 publications

A hierarchical parallel evolvable hardware based on network on chip

A hierarchical parallel evolvable hardware based on network on chip

Improved Task Scheduling on Parallel System using Genetic Algorithm

Variances Handling Method of Clinical Pathways Based on T-S Fuzzy Neural Networks with Novel Hybrid Learning Algorithm

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