Effects of learning rate on the performance of the population based incremental learning algorithm

Folly, Komla A.; Venayagamoorthy, G.K.

doi:10.1109/ijcnn.2009.5179080

Cited by 29 publications

(21 citation statements)

References 4 publications

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“…This biasvariance trade-off can be dealt by means of an adaptive learning rate. A common choice is to select a very low learning rate at the beginning of the search process and then increasing it linearly toward some maximum value (Folly and Venayagamoorthy, 2009).…”

Section: The Pbil Algorithmmentioning

confidence: 99%

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FPGA Implementation of a Multi-Population PBIL Algorithm

Coelho

Pinho

Boaventura‐Cunha

2015

Proceedings of the 7th International Joint Conference on Computational Intelligence

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Abstract:Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations.

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Section: The Pbil Algorithmmentioning

confidence: 99%

“…In (Folly and Venayagamoorthy, 2009) the effect of the learning rate on PBIL performance was evaluated within a power system controller design framework. The authors describe that, for high learning rate values, the population diversity is lost.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of a Multi-Population PBIL Algorithm

Coelho

Pinho

Boaventura‐Cunha

2015

Proceedings of the 7th International Joint Conference on Computational Intelligence

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“…In PBIL, there is no crossover operator [23] [24]. Instead a probability vector is used to create new trial solutions through learning [25] [26]. This probability vector is initially set to 0.5 to ensure that the probability vector is unbiased and the initial trial solutions created from the probability vector are completely random [23] [27].…”

Section: Overview Of Population-based Incremental Learningmentioning

confidence: 99%

“…The higher the value of the learning rate the faster the algorithm will converge towards an optimal solution (i.e., more exploitation). The lower the value of the learning rate the more exploration of the search space the algorithm will perform [26].…”

Section: Overview Of Population-based Incremental Learningmentioning

confidence: 99%

Application of Evolutionary Algorithm for Optimal Directional Overcurrent Relay Coordination

Stenane¹,

Folly²

2014

JCC

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In this paper, two Evolutionary Algorithms (EAs) i.e., an improved Genetic Algorithms (GAs) and Population Based Incremental Learning (PBIL) algorithm are applied for optimal coordination of directional overcurrent relays in an interconnected power system network. The problem of coordinating directional overcurrent relays is formulated as an optimization problem that is solved via the improved GAs and PBIL. The simulation results obtained using the improved GAs are compared with those obtained using PBIL. The results show that the improved GA proposed in this paper performs better than PBIL.

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“…Therefore, this question will be addressed in this study. Furthermore, it has been found [14] that learning rate was the most affective with search performance of PBIL. Another way to improve the search performance of MPBIL is to use an adaptive learning rate method [15].…”

Section: Introductionmentioning

confidence: 99%

Topology Optimisation Using MPBILs and Multi-Grid Ground Element

Sleesongsom

Bureerat

2018

Applied Sciences

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This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the multi and adaptive learning rate, respectively. Four classic multi-objective structural topology optimization problems are used for testing the performance. Furthermore, these topology optimization problems are improved by the method of multiple resolutions of ground elements, which is called a multi-grid approach (MG). Multi-objective design problems with MG design variables are then posed and tackled by the traditional MPBIL and its improved variants. The results show that using MPBIL with opposite-based concept and MG approach can outperform other MPBIL versions.

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Effects of learning rate on the performance of the population based incremental learning algorithm

Cited by 29 publications

References 4 publications

FPGA Implementation of a Multi-Population PBIL Algorithm

FPGA Implementation of a Multi-Population PBIL Algorithm

Application of Evolutionary Algorithm for Optimal Directional Overcurrent Relay Coordination

Topology Optimisation Using MPBILs and Multi-Grid Ground Element

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