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It is widely accepted that driver pathways offer important information for precision and personalized medicine of cancer treatment, hence the problems of driver pathways identification have become a research hotspot in bioinformatics. In this paper, an improved collaborative mutation driver pathways model ICMDP is proposed by integrating the somatic mutation, copy number variation and gene expression data. The model has two characteristics:(1) each individual pathway has moderate mutual exclusion and high coverage; (2) collaborative driver pathways exhibit significant common mutations in cancer samples, and the genes in collaborative driver pathways are related. Meanwhile, a parthenogenetic algorithm PA-ICMDP is proposed for solving the ICMDP model. Experiments were performed to compare algorithms PA-ICMDP, CoMDP and GAMTOC by using real biological data sets, i.e., the samples of glioblastoma and ovarian. The experimental results indicate that the PA-ICMDP algorithm can not only identify important collaborative driver pathways with higher co-occurrence mutation rates, but also detect more important driver genes such as MET, MDM2, GAB2, TERT, TBX3 and so on. In addition, the EICMDP model and the PA-EICMDP algorithm are put forward by extending the ICMDP model and the PAICMDP algorithm respectively. They can effectively identify other important pathways that collaborate with known driver pathways. The experimental results indicate that the methods presented in this paper may become suitable tools for mining driver genes and driver pathways related to cancer development.

The investigation of commonalities among different cancers is one of the important problems for deciphering cancers and will be helpful for personalized therapy in cancer treatment. Zhang et al. have presented the ComMDP method to solve this problem in 2017. However, when the number of samples among different cancers varies largely, accumulating the absolute weight value of every cancer, performed by the ComMDP method, may lead to missing some driver pathways. In this paper, an improved mathematical model is proposed by replacing the absolute weight values with the relative ratios of them, and introducing variance to minimize the dispersion of each ratio. By introducing a kind of short chromosome code and a greedy based recombination operator, a pathenogenetic algorithm PGA-MDP is put forward for solving this model. Experimental results indicate that the PGA-MDP algorithm is indeed able to detect some biologically meaningful gene sets which are missed by the ComMDP one. Hence it may become a useful complementary tool for identifying cancer pathways.

The maximum fragment length problem (MFL) is an effective mathematical model for solving the founder sequence reconstruction problem. Roli et al. proposed a constructive heuristic algorithm (it is named as RHRC in this paper) for solving the MFL model and a tabu search method for further optimizing the solution obtained by RHRC. RHRC algorithm produces uncertain solutions by introducing stochastic information. In this paper, an improved algorithm I-RHRC is presented with the explicit aim of providing ascertained solutions. Instead of using stochastic values, I-RHRC takes advantage of some potential information, i.e. the proportion of 0 and 1 entries in the column of the founder matrix and that of the recombinant matrix, and some other heuristic information, to get ascertained values. Experimental results show not only that I-RHRC algorithm can get better solutions than RHRC algorithm, but also that the tabu search method based on I-RHRC outperforms that based on RHRC.

In recent years, the problem of haplotyping a single individual has become one of the hottest areas of Computational Biology. The weighted minimum letter flips (WMLF) model is one of the important computational models for this problem. Due to the NP-hardness of the model, in this paper, a practical heuristic algorithm PGA-WMLF based on parthenogenetic algorithm (PGA) is presented to solve it. A kind of short chromosome code and an effective recombination operator are designed for the algorithm. Experiment results indicate that the algorithm is a good solution for WMLF model, and gets better performance than previous works.

Most algorithms proposed in the literature for individual haplotyping problem try to generate a single pair of haploytpes with the highest accuracy in terms of certain specific optimization criteria. However, due to the limitations of models, the result produced by these algorithms may not be the real best one, which leads to a low reconstruction rate for the pair of constructed haplotypes. This paper starts with a thorough analysis on the reasons for how the real best result can be lost during a haplotyping process based on the minimum error correction (MEC) model. We propose a new idea to reduce the probability of losing the best result by generating a small set of optimal results (which will be called an optimal result set), instead of a single optimal result. Based on this idea, a practical parthenogenetic algorithm PGA-SET is presented to solve the MEC model. The short chromosome code and small size population of PGA-SET algorithm ensure that it can obtain a relatively small optimal result set. Experimental results indicate that the set contains no more than 7 results in general, and which contains at least a pair of haplotypes that has higher reconstruction rate than those generated by previous algorithms solving the MEC model. This strongly suggests that the idea of generating a small optimal result set may effectively avoid losing the real best result, and PGA-SET algorithm is a practical method based on this idea.

The minimum error correction (MEC) model is one of the widely accepted computational model for single individual haplotype reconstruction problem, and it has been proved to be NP-complete by Lippert et al.. Qian et al. presented a particle swarm optimization (PSO) algorithm to solve the model, and the length of a particle code is equal to the number of fragments. However, there are hundreds and thousands of fragments in practical applications, the PSO algorithm based on this kind of long particle code can not obtain high reconstruction rate efficiently. In this paper, a practical heuristic algorithm based on genetic algorithm (GA) is presented to solve the problem. A kind of short chromosome code and an effective climb operator are designed for the algorithm. Experimental results indicate that the algorithm designed in this paper can get higher reconstruction rate than the PSO algorithm.

In this paper, a practical algorithm PS-MEC is presented based on the idea of generating a small set of optimal results to reduce the probability of losing the best result. We design a kind of short particle code for the algorithm by taking advantage of the low heterozygous frequency of single nucleotide polymorphisms. Experimental results indicate PS-MEC can get a set containing no more than four results in general, and which contains at least a pair of haplotypes that has higher reconstruction rate than those generated by previous algorithms solving the model. Moreover, PS-MEC is still efficient even for solving large size problems.

With the rapid development of e-commerce, logistic industry has also experienced a new reform. Intelligent logistics is an important part of it and plays a key role to realize highly effective logistics. In the process of logistics, there are abundant operational and decision-making problems that need to be solved, and logistic vehicle routing problem is one of which. However, the vehicle routing problem with time windows (VRPTM) is a combination optimization problem and is a NP-complete problem, so we can't get satisfying results when we use exact approaches and normal heuristic ones. In this paper, an improved genetic algorithm based on RC operator which is an improvement of Route Crossover (RC) is developed to solve the VRPTM. Computational experiments show that this improved algorithm can obtain a general optimality for all evaluated indexes on the premise of satisfying every customer's demand and its performance is superior to the genetic algorithm based on RC or partially mapped crossover (PMX).

The appearance of the switched Ethernet opens up the use of Ethernet as a reliable deterministic communication technology. Comparing with traditional Ethernet, the real-time characteristic of the switched Ethernet is more advanced because it provides a private collision domain to each port of a switch to avoid collisions. Based on the two embedded ARM controllers connected over the switched Ethernet, the real-time performance of four different protocols on ARM-Linux 2.4 and 2.6 platforms are assessed and discussed in this paper. The testing results show that the controllers connected over the switched Ethernet, with which using ARM-Linux and UDP or RTP protocol, to some extent has potential to be used in many real-time control applications, though some drawbacks still need to be improved. The other interesting point seen from the results is that there is a visible difference in real-time performance between ARM-Linux 2.4 and 2.6. This difference is ARM-Linux 2.6 has higher real-time performance than ARM-Linux 2.4. So ARM-Linux 2.6 in many networked control applications may be a better choice in many cases.