Evolutionary Computation and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 726

Special Issue Editor


E-Mail Website
Guest Editor
Centro de Tecnologías de la Imagen (CTIM), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain
Interests: computer vision; machine learning; optimization

Special Issue Information

Dear Colleagues,

Evolutionary computing has become a focus of attention during the last decades. Many interesting biologically inspired methods have been proposed to solve a wide variety of complex optimization problems. This rapidly evolving field has made tackling new applications possible in different areas of science, engineering, finance, or economics.

Optimization is present in many real-life problems. Thanks to the capabilities of modern computer systems, researchers face new challenges that require innovative technologies that go beyond the current state-of-the-art methods. A large number of methods, ranging from genetic and evolutionary algorithms to bio-inspired and multi-objective optimization, swarm intelligence, or metaheuristics, permit the solution of many types of problems in multiple areas. It is necessary to improve traditional methods or to propose new algorithms in order to cope with such demands. Focusing on applications is important for driving progress in this discipline and for solving increasingly complex and time-consuming problems.

The aim of this Special Issue is to bring together original contributions on the latest theories and applications of evolutionary computing. Articles with solid theoretical and practical contributions and with a focus on innovative applications in different areas of science, engineering, and economics are welcome.

Dr. Javier Sánchez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • genetic algorithms
  • evolutionary algorithms
  • genetic programming
  • evolutionary programming
  • multi-objective optimization
  • combinatorial optimization
  • bio-inspired optimization
  • swarm intelligence
  • differential evolution
  • metaheuristics

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

29 pages, 749 KiB  
Article
Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection
by Rodrigo Olivares, Camilo Ravelo, Ricardo Soto and Broderick Crawford
Mathematics 2024, 12(8), 1249; https://doi.org/10.3390/math12081249 - 20 Apr 2024
Cited by 1 | Viewed by 384
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Evolutionary Computation and Applications)
Show Figures

Figure 1

Back to TopTop