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Bioinformatics is a subject that studies life phenomena by using mathematical and information science theories and techniques. Its main tasks, such as DNA sequence comparison, protein structure prediction and cell metabolism analysis, can be regarded as complex optimization problems with different characteristics. Evolutionary computing is a kind of global optimization algorithm inspired by nature. Over the years, scholars have accumulated fruitful results in solving complex optimization problems such as large-scale, dynamic, multi-modal, multi-objective and multi-constrained problems by using Evolutionary Computation algorithms, and have successfully applied to the above optimization tasks in bioinformatics. This paper mainly summarizes the work of Evolutionary Computation technologies in bioinformatics from 2019 to 2023 at multiple levels, including Genomics, Proteomics, metabolomics and molecular networks related optimization tasks, as well as further applications in disease diagnosis and drug development. • Recent works on Evolutionary Computation technologies in Bioinformatics from 2019 to 2023. • Optimization tasks in Bioinformatics from genotype to phenotype. • Evolutionary Computation technologies from Genomics, Proteomics, Metabolomics and Molecular Networks level respectively. • Downstream applications such as disease diagnosis and drug discovery. [ABSTRACT FROM AUTHOR]

Adaptive operator selection is an online method that automatically adjusts the application rate of different operators based on their actual performance. This paper proposes an adaptive operator selection paradigm based on dueling deep Q-network (DDQN), aiming to improve the training efficiency of the Q-network for solving multi-objective optimization problems. The Q-network is decomposed into state value and action advantage networks, allowing the agent to learn state values more frequently and accurately. A novel state space and reward mechanism are designed to adapt to the characteristics of evolutionary multi-objective optimization. Combining adaptive operator selection with a multi-objective evolutionary algorithm with adaptive weights (AdaW), an AdaW-DDQN algorithm with high adaptability is proposed. Experimental results on three complex benchmark suites demonstrate that the proposed adaptive operator selection paradigm significantly improves the performance of multi-objective optimization algorithms. The AdaW-DDQN algorithm provides an effective and efficient solution for solving multi-objective optimization problems. • An adaptive operator selection paradigm based on dueling deep Q-network is proposed. • Ten effective crossover operators are designed as a pool of candidate operators. • A new state space and reward mechanism are designed for the multi-objective optimization problem. • The proposed adaptive operator selection paradigm is embedded into MOEA/D with adaptive weights. • Experimental results show that AdaW-DDQN has strong generalization ability. [ABSTRACT FROM AUTHOR]