The theory of genetics and evolution has made a material and regular explanation of the occurrence and development of the entire biological world. It introduced the idea of development and change into the biological world, and made a revolutionary change in biology. The theory of evolution not only promotes the development of genetics research but also greatly promotes the development of other disciplines. Inspired by the theory of genetics/evolution, scientists have proposed a large number of computational mechanisms based on genetic/evolutionary operators and random search mechanisms.
Recently, there are many optimization problems in the applications of genomic data where traditional mathematical method is not applicable anymore. Compared with traditional calculus-based methods and exhaustive methods, genetic/evolutionary operators can reach the global optimization with high robustness and wide applicability. With the characteristics of self-organization, self-adaptation and self-learning, genetic/evolutionary operators can effectively carry on complex optimization without limitation by the nature of problem. In the applications of genomic data, there are many sorts of analysis that involve complex patterns and complicated regulations, where the traditional optimization is not applicable. If the genomic analysis are empowered by genetic/evolutionary operators based complex optimization, a series of bottleneck bioinformatics tasks can benefit from this development. Consequently, this research topic aims at discussing the complex optimization problems with genetic/evolutionary operators in genomic data applications.
In this Research Topic, we would like to focus on the mechanism of genetic/evolutionary operators including but not limited to Genetic algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and their application to complex optimization problems for genomic data. For computational mechanism, typical topics include computational large-scale optimization problem, intelligent scheduling problem, and non-deterministic polynomial complete problem. For complex optimization in genomics, the applications of genetic/evolutionary operators and other types of optimizations are also welcome.
The theory of genetics and evolution has made a material and regular explanation of the occurrence and development of the entire biological world. It introduced the idea of development and change into the biological world, and made a revolutionary change in biology. The theory of evolution not only promotes the development of genetics research but also greatly promotes the development of other disciplines. Inspired by the theory of genetics/evolution, scientists have proposed a large number of computational mechanisms based on genetic/evolutionary operators and random search mechanisms.
Recently, there are many optimization problems in the applications of genomic data where traditional mathematical method is not applicable anymore. Compared with traditional calculus-based methods and exhaustive methods, genetic/evolutionary operators can reach the global optimization with high robustness and wide applicability. With the characteristics of self-organization, self-adaptation and self-learning, genetic/evolutionary operators can effectively carry on complex optimization without limitation by the nature of problem. In the applications of genomic data, there are many sorts of analysis that involve complex patterns and complicated regulations, where the traditional optimization is not applicable. If the genomic analysis are empowered by genetic/evolutionary operators based complex optimization, a series of bottleneck bioinformatics tasks can benefit from this development. Consequently, this research topic aims at discussing the complex optimization problems with genetic/evolutionary operators in genomic data applications.
In this Research Topic, we would like to focus on the mechanism of genetic/evolutionary operators including but not limited to Genetic algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and their application to complex optimization problems for genomic data. For computational mechanism, typical topics include computational large-scale optimization problem, intelligent scheduling problem, and non-deterministic polynomial complete problem. For complex optimization in genomics, the applications of genetic/evolutionary operators and other types of optimizations are also welcome.
