About this Research Topic
Bio-inspired computation is a computational intelligence technique based on principles or models of biological systems to solve complex real-world problems. The typical bio-inspired technologies include ant colony algorithms, bee algorithms, firefly algorithms, cuckoo search, particle swarm optimization and so on. Due to their incredible abilities to solve complex problems, they have attracted much attention from many scholars and have been successful applied in almost every area of science and engineering arranging from electronic engineering and mechanical engineering to molecular biology and medical engineering.
However, because of complexity and uncertainty in combinational optimization problems, it is difficult to find out the optimum solution using the bio-inspired technologies within limited time. As a result, it is necessary to design and assess new bio-inspired algorithms. Currently, although the existing biological heuristic algorithms have different heuristic objects, they all simulate the process of simple individuals collaborating to solve complex problems. Comparing with the existing ones, the new bio-inspired algorithms have more focus on parallelism and intersectionality, where the former means these new bio-inspired algorithms should have more effective optimization ability and the latter means combination of two or more biological heuristic algorithms such as immune genetic algorithm, genetic particle swarm optimization and so on. In addition, with the development of big data and cloud computing technology, bio-inspired computation with intelligent emergence mechanism will also be widely used in human brain, evolvable software, cloud service network and other emerging areas.
The Research Topic welcomes submissions on the new bio-inspired technologies and their applications. Potential topics include, but are not limited to:
• Benchmarking and evaluation of new bio-inspired algorithms
• Comparative theoretical and empirical studies on bio-inspired algorithms such as evolutionary algorithm, swarm intelligence, artificial immune system, etc.
• New bio-inspired methodology analysis tools, e.g. rough sets, stochastic process, etc.
• Bio-inspired algorithms for biological science problems including comparison and classification of biological sequences, optimization of shortest supersequence time, gene expression clustering and classification, gene selection, DNA fragment assembly, protein function prediction, construction of gene regulatory networks
• Bio-inspired algorithms for robot and human computer interaction (HCI), such as gesture recognition based on EMG, trajectory tracking and planning, grasp control, target detection, target identification
• Bio-inspired algorithms for mechanical engineering problems, such as optimal design, production planning and scheduling, vibration control, intelligent manufacturing, monitoring and diagnosis
• Problems related to design, evaluation and analysis of bio-inspired algorithms in human brain, evolvable software, cloud service network and other fields
However, because of complexity and uncertainty in combinational optimization problems, it is difficult to find out the optimum solution using the bio-inspired technologies within limited time. As a result, it is necessary to design and assess new bio-inspired algorithms. Currently, although the existing biological heuristic algorithms have different heuristic objects, they all simulate the process of simple individuals collaborating to solve complex problems. Comparing with the existing ones, the new bio-inspired algorithms have more focus on parallelism and intersectionality, where the former means these new bio-inspired algorithms should have more effective optimization ability and the latter means combination of two or more biological heuristic algorithms such as immune genetic algorithm, genetic particle swarm optimization and so on. In addition, with the development of big data and cloud computing technology, bio-inspired computation with intelligent emergence mechanism will also be widely used in human brain, evolvable software, cloud service network and other emerging areas.
The Research Topic welcomes submissions on the new bio-inspired technologies and their applications. Potential topics include, but are not limited to:
• Benchmarking and evaluation of new bio-inspired algorithms
• Comparative theoretical and empirical studies on bio-inspired algorithms such as evolutionary algorithm, swarm intelligence, artificial immune system, etc.
• New bio-inspired methodology analysis tools, e.g. rough sets, stochastic process, etc.
• Bio-inspired algorithms for biological science problems including comparison and classification of biological sequences, optimization of shortest supersequence time, gene expression clustering and classification, gene selection, DNA fragment assembly, protein function prediction, construction of gene regulatory networks
• Bio-inspired algorithms for robot and human computer interaction (HCI), such as gesture recognition based on EMG, trajectory tracking and planning, grasp control, target detection, target identification
• Bio-inspired algorithms for mechanical engineering problems, such as optimal design, production planning and scheduling, vibration control, intelligent manufacturing, monitoring and diagnosis
• Problems related to design, evaluation and analysis of bio-inspired algorithms in human brain, evolvable software, cloud service network and other fields
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.
