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Article
Learning to sample initial solution for solving 0–1 discrete optimization problem by local search
Local search methods are convenient alternatives for solving discrete optimization problems (DOPs). These easy-to-implement methods are able to find approximate optimal solutions within a tolerable time limit....
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Article
Learning to select the recombination operator for derivative-free optimization
Extensive studies on selecting recombination operators adaptively, namely, adaptive operator selection (AOS), during the search process of an evolutionary algorithm (EA), have shown that AOS is promising for i...
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Article
Open AccessSingle-cell profiling of response to neoadjuvant chemo-immunotherapy in surgically resectable esophageal squamous cell carcinoma
The efficacy of neoadjuvant chemo-immunotherapy (NAT) in esophageal squamous cell carcinoma (ESCC) is challenged by the intricate interplay within the tumor microenvironment (TME). Unveiling the immune landsca...
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Article
Open AccessThree chromosome-scale Papaver genomes reveal punctuated patchwork evolution of the morphinan and noscapine biosynthesis pathway
For millions of years, plants evolve plenty of structurally diverse secondary metabolites (SM) to support their sessile lifestyles through continuous biochemical pathway innovation. While new genes commonly dr...
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Article
Open AccessAberrantly high activation of a FoxM1–STMN1 axis contributes to progression and tumorigenesis in FoxM1-driven cancers
Fork-head box protein M1 (FoxM1) is a transcriptional factor which plays critical roles in cancer development and progression. However, the general regulatory mechanism of FoxM1 is still limited. STMN1 is a mi...
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Article
Open AccessHypoxia-sensitive long noncoding RNA CASC15 promotes lung tumorigenesis by regulating the SOX4/β-catenin axis
Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are involved in the hypoxia-related cancer process and play pivotal roles in enabling malignant cells to survive under hypoxic stress....
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Chapter and Conference Paper
Approximating Pareto Fronts in Evolutionary Multiobjective Optimization with Large Population Size
Approximating the Pareto fronts (PFs) of multiobjective optimization problems (MOPs) with a population of nondominated solutions is a common strategy in evolutionary multiobjective optimization (EMO). In the c...
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Chapter and Conference Paper
A Clustering-Based Multiobjective Evolutionary Algorithm for Balancing Exploration and Exploitation
This paper proposes a simple but promising clustering-based multi-objective evolutionary algorithm, termed as CMOEA. At each generation, CMOEA first divides the current population into several subpopulations b...
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Chapter and Conference Paper
Adjustment of Weight Vectors of Penalty-Based Boundary Intersection Method in MOEA/D
Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) is one of the dominant algorithmic frameworks for multi-objective optimization in the area of evolutionary computation. The performance of...
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Chapter and Conference Paper
Multi-objective Techniques for Single-Objective Local Search: A Case Study on Traveling Salesman Problem
In this paper, we show that the techniques widely used in multi-objective optimization can help a single-objective local search procedure escape from local optima and find better solutions. The Traveling Sales...
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Article
MOEA/D with chain-based random local search for sparse optimization
The goal in sparse approximation is to find a sparse representation of a system. This can be done by minimizing a data-fitting term and a sparsity term at the same time. This sparse term imposes penalty for sp...
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Article
Open AccesscircHIPK3 regulates cell proliferation and migration by sponging miR-124 and regulating AQP3 expression in hepatocellular carcinoma
Noncoding RNAs plays an important role in hepatocellular carcinoma (HCC). Here, we show that miR-124 was downregulated in HCC tissues and that the ectopic expression of miR-124 inhibited the proliferation and ...
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Chapter and Conference Paper
A Hybrid Discrete Artificial Bee Colony Algorithm for Multi-objective Blocking Lot-Streaming Flow Shop Scheduling Problem
A blocking lot-streaming flow shop (BLSFS) scheduling problem involves in splitting a job into several sublots and no capacity buffers with blocking between adjacent machines. It is of popularity in real-world...
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Chapter and Conference Paper
A New Steady-State MOEA/D for Sparse Optimization
The classical algorithms based on regularization usually solve sparse optimization problems under the framework of single objective optimization, which combines the sparse term with the loss term. The majority...
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Chapter and Conference Paper
A Reference-Inspired Evolutionary Algorithm with Subregion Decomposition for Many-Objective Optimization
In this paper, we propose a reference-inspired multiobjective evolutionary algorithm for many-objective optimisation. The main idea is (1) to summarise information inspired by a set of randomly generated refer...
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Article
Tropomyosin-related kinase B promotes distant metastasis of colorectal cancer through protein kinase B-mediated anoikis suppression and correlates with poor prognosis
An increasing amount of evidence demonstrated that the neurotrophic receptor tropomyosin-related kinase B (TrkB) plays a critical role in the development and progression of multiple types of cancer. However, i...
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Article
Grid-based implementation of XDS-I as part of image-enabled EHR for regional healthcare in Shanghai
Due to the rapid growth of Shanghai city to 20 million residents, the balance between healthcare supply and demand has become an important issue. The local government hopes to ameliorate this problem by develo...
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Article
Combinations of estimation of distribution algorithms and other techniques
This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems: a) guided mutation, an offspring generator ...
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Chapter and Conference Paper
Robust Visual Mining of Data with Error Information
Recent results on robust density-based clustering have indicated that the uncertainty associated with the actual measurements can be exploited to locate objects that are atypical for a reason unrelated to meas...
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Chapter
Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem
This chapter proposes a combination of estimation of distribution algorithm (EDA) and the 2-opt local search algorithm (EDA/LS) for the quadratic assignment problem (QAP). In EDA/LS, a new operator, called guided...
