scholarly journals FPGA Implementation of a Cellular Compact Genetic Algorithm

2008 ◽  
Author(s):  
Yutana Jewajinda ◽  
Prabhas Chongstitvatana
Keyword(s):  
Genetic Algorithm ◽  
Fpga Implementation ◽  
Compact Genetic Algorithm

scholarly journals A GPU-Enabled Compact Genetic Algorithm for Very Large-Scale Optimization Problems

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 758
Author(s):  
Andrea Ferigo ◽  
Giovanni Iacca
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Space ◽  
Large Scale Problems ◽  
Processing Power ◽  
Compact Genetic Algorithm ◽  
Estimation Of Distribution ◽  
Feasible Solutions ◽  
Compact Optimization

The ever-increasing complexity of industrial and engineering problems poses nowadays a number of optimization problems characterized by thousands, if not millions, of variables. For instance, very large-scale problems can be found in chemical and material engineering, networked systems, logistics and scheduling. Recently, Deb and Myburgh proposed an evolutionary algorithm capable of handling a scheduling optimization problem with a staggering number of variables: one billion. However, one important limitation of this algorithm is its memory consumption, which is in the order of 120 GB. Here, we follow up on this research by applying to the same problem a GPU-enabled “compact” Genetic Algorithm, i.e., an Estimation of Distribution Algorithm that instead of using an actual population of candidate solutions only requires and adapts a probabilistic model of their distribution in the search space. We also introduce a smart initialization technique and custom operators to guide the search towards feasible solutions. Leveraging the compact optimization concept, we show how such an algorithm can optimize efficiently very large-scale problems with millions of variables, with limited memory and processing power. To complete our analysis, we report the results of the algorithm on very large-scale instances of the OneMax problem.

Application and Need-Based Architecture Design of Deep Neural Networks

2020 ◽  
Vol 34 (13) ◽  
pp. 2052014 ◽  
Author(s):  
Soniya ◽  
Sandeep Paul ◽  
Lotika Singh
Keyword(s):  
Genetic Algorithm ◽  
Network Structure ◽  
Gradient Descent ◽  
Stochastic Gradient Descent ◽  
Number Of Layers ◽  
Effective Manner ◽  
Compact Genetic Algorithm ◽  
Benchmark Datasets ◽  
The Cost ◽  
Optimal Set

This paper applies a hybrid evolutionary approach to a convolutional neural network (CNN) and determines the number of layers and filters based on the application and user need. It integrates compact genetic algorithm with stochastic gradient descent (SGD) for simultaneously evolving structure and parameters of the CNN. It defines an effectual string representation for combining structure and parameters of the CNN. The compact genetic algorithm helps in the evolution of network structure by optimizing the number of convolutional layers and number of filters in each convolutional layer. At the same time, an optimal set of weight parameters of the network is obtained using the SGD law. This approach amalgamates exploration in network space by compact genetic algorithm and exploitation in weight space with SGD in an effective manner. The proposed approach also incorporates user-defined parameters in the cost function in an elegant manner which controls the network structure and hence the performance of the network based on the users need. The effectiveness of the proposed approach has been demonstrated on four benchmark datasets, namely MNIST, COIL-100, CIFAR-10 and CIFAR-100. The obtained results clearly demonstrate the potential of the proposed approach by evolving architectures based on the nature of the application and the need of the user.

A Simple Real-Coded Extended Compact Genetic Algorithm

2007 ◽  
Author(s):  
Luca Fossati ◽  
Pier Luca Lanzi ◽  
Kumara Sastry ◽  
David E. Goldberg ◽  
Osvaldo Gomez
Keyword(s):  
Genetic Algorithm ◽  
Compact Genetic Algorithm
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