The convergence of cognitive neuroscience with machine learning and computational modelling, alongside the study of natural phenomena through bio-inspired optimization algorithms, has unlocked a whole new realm of possibilities across artificial intelligence. This fusion initiate’s novel research areas, particularly focusing on the integration of learning-based methods with bio-inspired optimization techniques such as Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Ant Colony System. These methods, grounded in both human cognition and behavior as well as the mechanics of biological systems, aim to achieve efficient solutions to complex optimization problems in significantly less time through cooperative virtual agents sharing knowledge about the resolution process.
This rapidly evolving area represents a significant frontier in neuroscience, machine learning, and artificial intelligence, where the hybridization of bio-inspired algorithms with intelligence mechanics from various fields uses data to enhance both local and global search procedures, leading towards more promising zones. Techniques based on context and environment learning further allow the design of reactive bio-inspired optimization algorithms capable of auto-governing behavior through parameter self-tuning or narrowing the solution space.
This research topic aims to comprehensively explore and highlight the latest developments in integrating learning-based methods with bio-inspired optimization algorithms, shedding light on new directions and challenges in this interdisciplinary field. We hope to inspire innovative problem-solving strategies, promote further academic discourse, and compile a high-quality corpus of original research, review articles, and case studies that delve into these hybrid models' potential and real-world implications.
Submissions are welcomed across a spectrum of focus areas, including but not limited to:
• Exploration of novel bio-inspired optimization algorithms grounded in neuroscience.
• Advancements in learning techniques based on the principles of human cognition.
• Application scenarios demonstrating the effectiveness of hybrid models.
• Theoretical discussions exploring the implications and challenges in this domain.
• Experimental studies detailing biomedical or neurological research outcomes using these algorithms.
We invite original research papers, case studies, review articles, and investigative reports that adhere to Frontiers' research integrity guidelines, aiming to enrich the growing body of knowledge at the intersection of neuroscience, AI, machine learning, and bio-inspired optimization. This Research Topic is devoted to publishing high-quality papers that employ hybridization to solve complex engineering problems, with reviews on this topic also welcome.
Keywords:
cognitive neuroscience, bio-inspired optimization algorithms, machine learning, hybrid algorithms, learning-based solvers
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
The convergence of cognitive neuroscience with machine learning and computational modelling, alongside the study of natural phenomena through bio-inspired optimization algorithms, has unlocked a whole new realm of possibilities across artificial intelligence. This fusion initiate’s novel research areas, particularly focusing on the integration of learning-based methods with bio-inspired optimization techniques such as Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Ant Colony System. These methods, grounded in both human cognition and behavior as well as the mechanics of biological systems, aim to achieve efficient solutions to complex optimization problems in significantly less time through cooperative virtual agents sharing knowledge about the resolution process.
This rapidly evolving area represents a significant frontier in neuroscience, machine learning, and artificial intelligence, where the hybridization of bio-inspired algorithms with intelligence mechanics from various fields uses data to enhance both local and global search procedures, leading towards more promising zones. Techniques based on context and environment learning further allow the design of reactive bio-inspired optimization algorithms capable of auto-governing behavior through parameter self-tuning or narrowing the solution space.
This research topic aims to comprehensively explore and highlight the latest developments in integrating learning-based methods with bio-inspired optimization algorithms, shedding light on new directions and challenges in this interdisciplinary field. We hope to inspire innovative problem-solving strategies, promote further academic discourse, and compile a high-quality corpus of original research, review articles, and case studies that delve into these hybrid models' potential and real-world implications.
Submissions are welcomed across a spectrum of focus areas, including but not limited to:
• Exploration of novel bio-inspired optimization algorithms grounded in neuroscience.
• Advancements in learning techniques based on the principles of human cognition.
• Application scenarios demonstrating the effectiveness of hybrid models.
• Theoretical discussions exploring the implications and challenges in this domain.
• Experimental studies detailing biomedical or neurological research outcomes using these algorithms.
We invite original research papers, case studies, review articles, and investigative reports that adhere to Frontiers' research integrity guidelines, aiming to enrich the growing body of knowledge at the intersection of neuroscience, AI, machine learning, and bio-inspired optimization. This Research Topic is devoted to publishing high-quality papers that employ hybridization to solve complex engineering problems, with reviews on this topic also welcome.
Keywords:
cognitive neuroscience, bio-inspired optimization algorithms, machine learning, hybrid algorithms, learning-based solvers
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.