Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection
Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an...
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ftdoajarticles:oai:doaj.org/article:7fce1ec84d58446082a12b3637535652 2024-09-15T18:28:45+00:00 Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection Rodrigo Olivares Camilo Ravelo Ricardo Soto Broderick Crawford 2024-04-01T00:00:00Z https://doi.org/10.3390/math12081249 https://doaj.org/article/7fce1ec84d58446082a12b3637535652 EN eng MDPI AG https://www.mdpi.com/2227-7390/12/8/1249 https://doaj.org/toc/2227-7390 doi:10.3390/math12081249 2227-7390 https://doaj.org/article/7fce1ec84d58446082a12b3637535652 Mathematics, Vol 12, Iss 8, p 1249 (2024) biomimetic orca predator algorithm deep reinforcement learning feature selection Mathematics QA1-939 article 2024 ftdoajarticles https://doi.org/10.3390/math12081249 2024-08-05T17:49:32Z Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy’s capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms. Article in Journal/Newspaper Orca Directory of Open Access Journals: DOAJ Articles Mathematics 12 8 1249 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
topic |
biomimetic orca predator algorithm deep reinforcement learning feature selection Mathematics QA1-939 |
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biomimetic orca predator algorithm deep reinforcement learning feature selection Mathematics QA1-939 Rodrigo Olivares Camilo Ravelo Ricardo Soto Broderick Crawford Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection |
topic_facet |
biomimetic orca predator algorithm deep reinforcement learning feature selection Mathematics QA1-939 |
description |
Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy’s capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms. |
format |
Article in Journal/Newspaper |
author |
Rodrigo Olivares Camilo Ravelo Ricardo Soto Broderick Crawford |
author_facet |
Rodrigo Olivares Camilo Ravelo Ricardo Soto Broderick Crawford |
author_sort |
Rodrigo Olivares |
title |
Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection |
title_short |
Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection |
title_full |
Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection |
title_fullStr |
Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection |
title_full_unstemmed |
Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection |
title_sort |
escaping stagnation through improved orca predator algorithm with deep reinforcement learning for feature selection |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/math12081249 https://doaj.org/article/7fce1ec84d58446082a12b3637535652 |
genre |
Orca |
genre_facet |
Orca |
op_source |
Mathematics, Vol 12, Iss 8, p 1249 (2024) |
op_relation |
https://www.mdpi.com/2227-7390/12/8/1249 https://doaj.org/toc/2227-7390 doi:10.3390/math12081249 2227-7390 https://doaj.org/article/7fce1ec84d58446082a12b3637535652 |
op_doi |
https://doi.org/10.3390/math12081249 |
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Mathematics |
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12 |
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8 |
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1249 |
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1810470200568446976 |