FPGA Implementation of a Cellular Compact Genetic Algorithm

In this paper we show a novel and efficient design of a compact Genetic Algorithm (cGA) in Hardware. This design presents the following features: modularity, concurrency, minimal resource consumption, real time execution, and high scalability properties. According to the obtained results, we show that it is viable to have this search algorithm in Hardware to be used in real time applications. Keywords-Compact Genetic Algorithm; FPGA design2010 International Conference on Reconfigurable Computing 978-0-7695-4314-7/10 $26.00

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“…In [3] it was proposed a Cellular Compact Genetic Algorithm implemented in a FPGA. It consists of a set of identical cGA.…”

Section: Previous Related Workmentioning

confidence: 99%

“…On the other hand, Compact Genetic Algorithms (cGA) are a kind of probabilistic model-building genetic algorithms or the Estimation Distribution Algorithms (EDA) [3]. cGA operates on probability vectors by replacing the variation operators (crossover and mutation) that describes the distribution of a hypothetic population of solutions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hardware Implementation of the Compact Genetic Algorithm

Moreno-Armendáriz

Cruz-Cortés

León-Javier

2010

2010 International Conference on Reconfigurable Computing and FPGAs

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“…In addition to a considerable number of conventional GA systems mentioned in [4], [5], [6], [20], FPGA-based master-slave GAs and dGAs have been demonstrated [7], [8], [9], [10], [11]. FPGA-based cGAs are also proposed in [12], [13], [14]. In the top 6 rows of Table II we summarise the features of these existing pGA systems.…”

Section: Fpga-based Parallel Gamentioning

confidence: 99%

“…There have been previous attempts to adapt pGAs to FPGAs for acceleration or low power consumption [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]. However, designing an FPGA-based pGA is not as easy as implementing multiple hardware blocks supporting a set of GA instances.…”

Section: Introductionmentioning

confidence: 99%

Automated framework for FPGA-based parallel genetic algorithms

Guo

Thomas

Guo

et al. 2014

2014 24th International Conference on Field Programmable Logic and Applications (FPL)

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Abstract-Parallel genetic algorithms (pGAs) are a variant of genetic algorithms which can promise substantial gains in both efficiency of execution and quality of results. pGAs have attracted researchers to implement them in FPGAs, but the implementation always needs large human effort. To simplify the implementation process and make the hardware pGA designs accessible to potential non-expert users, this paper proposes a generalpurpose framework, which takes in a high-level description of the optimisation target and automatically generates pGA designs for FPGAs. Our pGA system exploits the two levels of parallelism found in GA instances and genetic operations, allowing users to tailor the architecture for resource constraints at compile-time. The framework also enables users to tune a subset of parameters at run-time without time-consuming recompilation. Our pGA design is more flexible than previous ones, and has an average speedup of 26 times compared to the multi-core counterparts over five combinatorial and numerical optimisation problems. When compared with a GPU, it also shows a 6.8 times speedup over a combinatorial application.

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“…Several proposals for hardware implementation of evolutionary algorithms have been realized, such as Micro Algorithm [19][20][21][22][23] and Compact Genetic Algorithm (cGA) [24][25][26][27][28] with the aim of low resource consumption and minimal response time implementation. These algorithms lose the generality of solving problems of any kind, however such deployments have had success in combinatorial problems.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Differential Evolution Algorithm on FPGA for Double-Precision Floating-Point Representation

2014

APH

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FPGA Implementation of a Cellular Compact Genetic Algorithm

Abstract: Abstract

Cited by 16 publications

References 15 publications

A Novel Hardware Implementation of the Compact Genetic Algorithm

A Novel Hardware Implementation of the Compact Genetic Algorithm

Automated framework for FPGA-based parallel genetic algorithms

Design and Implementation of Differential Evolution Algorithm on FPGA for Double-Precision Floating-Point Representation

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