Evolutionary Computation based Feature Selection: A Survey

Dara, Suresh; Reddy, Mamidi Jagadeeshwara; Eluri, Nageswara Rao

doi:10.1109/iceca.2018.8474568

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…The proposed approach aims to implement an evolutionary-based learning strategy 46,47 to avoid premature convergence and improve diversity in the population. Once the MGSA model runs for some t′ -iterations, the positions of the different agents get updated.…”

Section: Implementation Of the Proposed Mgsa-evolutionary Approachmentioning

confidence: 99%

Humanoid path planning on even and uneven terrains using an efficient memory-based gravitational search algorithm and evolutionary learning strategy

Vikas

Parhi

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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The increasing demand for automation and material transportation has shown an incline toward optimal path navigation. The present work implements an intelligent Memory-based gravitational search algorithm (MGSA) with an evolutionary learning strategy to achieve a globally optimal collision-free path. The Evolutionary learning strategy helps improve the diversity among the Gravitational masses/agents, hence improving the overall exploration capability of the model. While the other approaches focus more on an evolutionary strategy based on mutation and cross-overs, the present technique implements the evolutionary strategy based on the position of the fit agents to improve the position of the unfit agents in the population. It ensures a fast-converging path planning result with an improved trajectory. Further, adding a memory-based approach helps the model remember the location of the best agent within the population. The controller is tested with multiple Humanoids on even and uneven terrains and showed a minimal improvement of more than 4% in path length with a minimum 5% deviation in the simulation and experimental results. The proposed approach showed a further improvement of more than 6% compared to the different intelligent path-planning approaches in a similar environment.

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Section: Implementation Of the Proposed Mgsa-evolutionary Approachmentioning

confidence: 99%