Parallel Genetic Algorithms for Architecture Optimization of Neural Networks for Pattern Recognition

Cárdenas, Martha Ivón; Melín, Patricia; Cruz, Laura M.

doi:10.1007/978-3-642-15111-8_19

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Besides the application domains previously highlighted, in the last few years parallel implementations of metaheuristics have been also successfully applied in many other areas such as energy and power network optimization (Peng et al., ; Zhao et al., ), health and medicine (Karnan and Gopal, ), strategic and military applications (Gao et al., ), economy and finance (Liu et al., ), workforce planning (Alba et al., ), and image processing (Cardenas et al., ; Harding and Banzhaf, ; Peng et al., ) and many other optimization problems (Alba et al., ; Crainic et al., ). This shows the growing research in parallel metaheuristics, and therefore we can conclude that the near future will witness many more real‐life situations and problems tackled using parallel metaheuristic algorithms.…”

Section: Modern Applications Solved By Parallel Metaheuristicsmentioning

confidence: 99%

“…Some parallel metaheuristics with recent implementations of the fine‐grain model in multicore architectures includes EAs (Munawar et al., ), EDAs (Perez et al., ), MOEAs (Nebro and Durillo, ), ACOs (Tsutsui and Fujimoto, ), VND/ILS (Subramanian et al., ), TS and several other metaheuristics (Bozejko et al., ). The master–slave model for parallel metaheuristics has also been implemented in multicore processors, for ACO (Delisle et al., , ; Guo et al., ; López‐Ibánez, ; Tsutsui, ; Tsutsui and Fujimoto, ), EA (Cardenas et al., ), TS and branch and bound (Hung and Chen, ); in these methods, the main advantage is the ability of computing the fitness evaluation in parallel by using several threads. Multicore multipopulation methods have also been proposed for several metaheuristics, such as ACO (Delisle et al., ; Gao et al., ; Li et al., ; Lucka and Piecka, ; Xiong et al., , ), EAs (Byun et al., ; He et al., ; Tsutsui, ), PSO (Tu and Liang, ), and the parallel artificial bee colony algorithm (Narasimhan, ).…”

Section: Technologies For Parallel Metaheuristicsmentioning

confidence: 99%

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Parallel metaheuristics: recent advances and new trends

Alba

Luque

Nesmachnow

2012

Int Trans Operational Res

269

118

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The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, we review these recent research areas in connection to parallel metaheuristics, as well as we identify future trends and possible open research lines for groups and PhD students.

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Section: Modern Applications Solved By Parallel Metaheuristicsmentioning

confidence: 99%

Section: Technologies For Parallel Metaheuristicsmentioning

confidence: 99%

Parallel metaheuristics: recent advances and new trends

Alba

Luque

Nesmachnow

2012

Int Trans Operational Res

269

118

View full text Add to dashboard Cite

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“…Reviews can be found in [24,[27][28][29]35]. Cardenas et al [53] presented the architecture optimization of neural networks using parallel genetic algorithms for pattern recognition based on person faces. They compared the results of the training stage for sequential and parallel implementations.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Pattern Storage Network of Associative memory with Sub-optimal GA for Hand written Hindi SWARS

Lavania

Singh

2013

IJMIT

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In this paper we are performing the evaluation of Hopfield neural network as Associative memory for recalling of memorized patterns from the Sub-optimal genetic algorithm for Handwritten of Hindi language. In this process the genetic algorithm is employed from sub-optimal form for recalling of memorized patterns corresponding to the presented noisy prototype input patterns. The sub-optimal form of GA is considered as the non-random initial population or solution. So, rather than random start, the GA explores from the sum of correlated weight matrices for the input patterns of training set. The objective of this study is to determine the optimal weight matrix for correct recalling corresponds to approximate prototype input pattern of Hindi â€˜SW. In this study the performance of neural network is evaluated in terms of the rate of success for recalling of memorized Hindi for presented approximate prototype input pattern with GA in two aspects. The first aspect reflects the random nature of the GA and the second one exhibit the suboptimal nature of the GA for its exploration.The simulated results demonstrate the better performance of network for recalling of the memorized Hindi SWARS using genetic algorithm to evolve the population of weights from sub-optimal weight matrix.

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