Search

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]

Natural Language Processing (NLP) models are known vulnerable to adversarial text attacks. Various word-level attacks have been proposed to modify input words by a pre-calculated word saliency order or the heuristic optimization algorithm. However, the predefined fixed order usually leads to a low attack success ratio, and the heuristic algorithm is often inefficient due to many model queries. In this paper, an Adaptive Gradient-based Word Saliency (AGWS) is proposed to improve the query-efficiency and simultaneously preserve a high attack success ratio for the word-level adversarial text attack. Firstly, the AGWS calculates the word saliency with a one-time gradient to all words instead of iterative queries, so the query-efficiency is enhanced. Secondly, the AGWS adaptively updates the word saliency via the variable neighborhood search algorithm to avoid the fixed modification order, which is significant in improving the attack success ratio. Additionally, the AGWS collect substitutes with a hybrid sememe and synonym strategy to enlarge adversary options. Extensive experiments and ablation studies manifest that the AGWS achieves the highest or second highest attack success ratio with lowest word perturbation percentage and improves the query-efficiency compared with baselines. Besides, the AGWS also shows superiorities in adversarial training and transferability. [ABSTRACT FROM AUTHOR]

Trust region policy optimization (TRPO) is one of the landmark policy optimization algorithms in deep reinforcement learning. Its purpose is to maximize a surrogate objective based on an advantage function, subject to the limited Kullback–Leibler (KL) divergence of two consecutive policies. Although there have been many successful applications of this algorithm in the literature, the approach has often been criticized for suppressing the exploration ability of some application environments due to its strict divergence constraint. As such, most researchers prefer to use entropy regularization, which is added to the expected discounted rewards or the surrogate objectives. That said, there is much debate about whether there might be an alternative strategy for regularizing TRPOs. In this paper, we present just that. Our strategy is to regularize the KL divergence-based constraint via Shannon entropy. This approach enlarges the difference between two consecutive policies and thus derives a new TRPO scheme with entropy regularization for use with KL divergence constraint. Next, the surrogate objective and Shannon entropy are approximated linearly, while the KL divergence is expanded quadratically. An efficient conjugate gradient optimization procedure then solves two sets of linear equations, providing a detailed code-level implementation that can be used for a fair experimental comparison. Extensive experiments within eight benchmark environments demonstrate that our proposed method is superior to both the original TRPO and the entropy regularized objective TRPO. Further, theoretical and experimental analysis shows that three TRPO-like methods have an equal time complexity and a close computational burden. [ABSTRACT FROM AUTHOR]

Combinatorial optimization problems (COPs) play an important role in both industrial production and theoretical research. As a classical class of optimization algorithms, evolutionary algorithms have shown good performance in COPs. However, as the size of the problem increases, it becomes difficult for evolutionary algorithms to produce satisfactory solutions within the specified time. In recent years, neural network algorithms have been applied to solve COPs and have shown superior effects. Inspired by this, this paper proposes a new algorithm based on graph structure for solving COPs. Through relaxation, COPs are transformed into their corresponding continuous optimization problems. The output state of our algorithm is converted to a discrete solution by approximation method. Compared with previous algorithms, our algorithm can not only solve COPs with general form such as binary quadratic programming (BQP), but also solve COPs with special form, which greatly expands the application scope of neural network algorithms. In addition, our algorithm can be embedded into other problem frameworks such as solving multi-objective combinatorial optimization problem (MOCOP) through scalarization method. A new penalty function is added to the objective function, which enables our algorithm to overcome the inconsistency between the optimal solution of the original problem and the corresponding continuous problem, so that our algorithm can perform well on various problems. Several numerical simulations are used to illustrate the effectiveness of our algorithm in solving COPs. Experimental results show that our algorithm can effectively solve COPs with general form and has a shorter solving time compared to traditional evolutionary algorithms. [ABSTRACT FROM AUTHOR]

The expensive optimization problem, widespread in real-world applications, poses great challenges to traditional evolutionary algorithms (EAs) due to its costly evaluations. A lot of investigations have proved that it is a practical way to incorporate surrogate models into EAs to reduce the number of time-consuming evaluations. Although various types of surrogate models from the community of machine learning, such as regression models that approximate the fitness values of candidate solutions or classification models that predict candidate solutions' category, have been widely applied separately, none of them consistently outperforms the other. Considering the intrinsic consistency of alleviating computational burden to improve the optimization efficiency, this paper introduces a two-layer architecture to integrate regression and classification models and synergize global and local evolution for high-dimensional expensive many-objective optimization problem (EMaOP). To implement this idea, the Kriging model based on incremental learning is utilized to explore the potential promising space. Further, multi local support vector machine (SVM) classifiers assisted improved differential evolution is run by turn with the global search to accelerate convergence. Accordingly, a novel surrogate-assisted EA, named Classification- and Regression-assisted evolutionary algorithm (CR-SAEA), is proposed on this basis. Experimental studies on several benchmark functions with up to 11 objectives and 200 dimensions confirm the effectiveness of the proposed algorithm and its competitiveness against state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel population robustness-based switching response framework (PR-SRF) is proposed to develop effective dynamic multi-objective optimization algorithm (DMOA), which integrates different response strategies to comprehensively cope with the dynamic behaviors. In particular, the population robustness is described by the quantification of how severely the environmental changes affect current population, which is timely graded as three levels of weak, strong, and normal to enable the adaptive switch of three different responses of diversity-enhancement, diversity-maintenance, and the knowledge-transfer, respectively. In this way, associations between the adopted responses and the changing environments are successfully established, thereby facilitating more intelligent decision when handling the dynamic behaviors. According to the benchmark evaluation results, the proposed PR-SRF-DMOA yields better comprehensive performance than several other DMOAs with popular response strategies, and it also outperforms another three DMOAs with hybrid responses, which demonstrates the great competitiveness of our algorithm. In addition, ablation study proves that the proposed PR-SRF can sufficiently exploit the merits of different responses, which effectively alleviates the negative knowledge transfer in extremely fluctuating environments, thereby providing valuable references for the development of evolutionary transfer optimization (ETO) algorithms. [ABSTRACT FROM AUTHOR]

Existing supervised Multi-Target Feature Selection (MTFS) methods seldom consider the nearest-neighbor relationship and statistical correlation of samples underlying the output space, which leads the result of feature selection to be easily interfered by the output noise, thus making it difficult to achieve satisfactory performance. This paper proposes a novel MTFS method to preserve both the global and local target correlations. Specifically, the low-rank constraint is introduced to achieve multi-layer regression structure to better decouple the inter-input and inter-target relationships. Moreover, the local nearest-neighbor relationships and variable correlations of the sample points in the output space are also explored through adaptive graph and manifold learning, to better utilize the target correlations to improve the MTFS performance. Following the above principle, the resulting objective function and the corresponding optimization algorithm are proposed. Extensive experiments on several public datasets show that the proposed method is superior to other state-of-the-art methods. • A new Embedded MTFS method is proposed to learn the feature redundancy and target correlations in MTR problems. • The nearest-neighbor relationships and target correlations are introduced to achieve superior performance in MTFS tasks. • Extensive experiments on several public datasets show that the proposed method is superior to the compared methods. [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]