An Adaptive Framework to Tune the Coordinate Systems in Nature-Inspired Optimization Algorithms

The performance of many nature-inspired optimization algorithms (NIOAs) depends strongly on their implemented coordinate system. However, the commonly used coordinate system is fixed and not well suited for different function landscapes, NIOAs thus might not search efficiently. To overcome this shortcoming, in this paper we propose a framework, named ACoS, to adaptively tune the coordinate systems in NIOAs. In ACoS, an Eigen coordinate system is established by making use of the cumulative population distribution information, which can be obtained based on a covariance matrix adaptation strategy and an additional archiving mechanism. Since the population distribution information can reflect the features of the function landscape to some extent, NIOAs in the Eigen coordinate system have the capability to identify the modality of the function landscape. In addition, the Eigen coordinate system is coupled with the original coordinate system, and they are selected according to a probability vector. The probability vector aims to determine the selection ratio of each coordinate system for each individual, and is adaptively updated based on the collected information from the offspring. ACoS has been applied to two of the most popular paradigms of NIOAs, i.e., particle swarm optimization and differential evolution, for solving 30 test functions with 30D and 50D at the 2014 IEEE Congress on Evolutionary Computation. The experimental studies demonstrate its effectiveness

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Copyright @ Springer-Verlag 2008Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant Nos. 70431003 and 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, and the National Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01

Topological analysis of functional connectivity in Parkinson’s disease

Parkinson’s disease (PD) is a clinically heterogeneous disorder, which mainly affects patients’ motor and non-motor function. Functional connectivity was preliminary explored and studied through resting state functional magnetic resonance imaging (rsfMRI). Through the topological analysis of 54 PD scans and 31 age-matched normal controls (NC) in the Neurocon dataset, leveraging on rsfMRI data, the brain functional connection and the Vietoris-Rips (VR) complex were constructed. The barcodes of the complex were calculated to reflect the changes of functional connectivity neural circuits (FCNC) in brain network. The 0-dimensional Betti number β0 means the number of connected branches in VR complex. The average number of connected branches in PD group was greater than that in NC group when the threshold δ ≤ 0.7. Two-sample Mann–Whitney U test and false discovery rate (FDR) correction were used for statistical analysis to investigate the FCNC changes between PD and NC groups. In PD group, under threshold of 0.7, the number of FCNC involved was significantly differences and these brain regions include the Cuneus_R, Lingual_R, Fusiform_R and Heschl_R. There are also significant differences in brain regions in the Frontal_Inf_Orb_R and Pallidum_R, when the threshold increased to 0.8 and 0.9 (p < 0.05). In addition, when the length of FCNC was medium, there was a significant statistical difference between the PD group and the NC group in the Neurocon dataset and the Parkinson’s Progression Markers Initiative (PPMI) dataset. Topological analysis based on rsfMRI data may provide comprehensive information about the changes of FCNC and may provide an alternative for clinical differential diagnosis

A particle swarm optimization based memetic algorithm for dynamic optimization problems

Copyright @ Springer Science + Business Media B.V. 2010.Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant No. 70431003 and Grant No. 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, the National Support Plan of China under Grant No. 2006BAH02A09 and the Ministry of Education, science, and Technology in Korea through the Second-Phase of Brain Korea 21 Project in 2009, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and the Hong Kong Polytechnic University Research Grants under Grant G-YH60

Analysis of Driver Mutations in Female Non-Smoker Asian Patients with Pulmonary Adenocarcinoma

Amory Company; Science and Technology Commission of Shanghai Municipality [06DZ19502]Previous studies have revealed that EGFR mutation and/or EML4-ALK gene fusion rate was higher in the non-smoker Asian females with pulmonary adenocarcinoma. The aim of this study is to determine the distribution of known oncogenic driver mutations in the female non-smoker Asian patients with pulmonary adenocarcinoma. 104 consecutively resected lung adenocarcinomas from 396 non-smoker females (less than 100 cigarettes in a lifetime) at a single institution (Tongji University, Shanghai, China) were analyzed for mutations in EGFR, EML4-ALK, KRAS, HER2, BRAF, and PIK3CA. 73 (70.2 %) tumors harbored EGFR mutations; among these, 28 were deletions in exon 19, 44 were L858R missense changes, and eight were T790M mutations. 10 (9.6 %) harbored EML4-ALK fusions, two harbored KRAS mutations, two harbored BRAF mutations, and two harbored PI3K mutations. A majority of the mutations were mutually exclusive, except two with EGFR mutation and BRAF mutation, one with EML4-ALK fusions and PI3K mutation. Thus, 82.7 % (86 of 104; 95 % CI, 75.4-90.0 %) of lung adenocarcinomas from non-smoker females were found to harbor the well-known oncogenic mutations in five genes. Lung cancer in non-smoking Asian females is a distinct entity, with majority of this subgroup being developed by the oncogenic mutations. The prospective mutation examination in this population will be helpful for devising a targeted therapy for a majority of the patients

Detection of copy number variations in rice using array-based comparative genomic hybridization

<p>Abstract</p> <p>Background</p> <p>Copy number variations (CNVs) can create new genes, change gene dosage, reshape gene structures, and modify elements regulating gene expression. As with all types of genetic variation, CNVs may influence phenotypic variation and gene expression. CNVs are thus considered major sources of genetic variation. Little is known, however, about their contribution to genetic variation in rice.</p> <p>Results</p> <p>To detect CNVs, we used a set of NimbleGen whole-genome comparative genomic hybridization arrays containing 718,256 oligonucleotide probes with a median probe spacing of 500 bp. We compiled a high-resolution map of CNVs in the rice genome, showing 641 CNVs between the genomes of the rice cultivars 'Nipponbare' (from <it>O. sativa </it>ssp. <it>japonica</it>) and 'Guang-lu-ai 4' (from <it>O. sativa </it>ssp. <it>indica</it>). The CNVs identified vary in size from 1.1 kb to 180.7 kb, and encompass approximately 7.6 Mb of the rice genome. The largest regions showing copy gain and loss are of 37.4 kb on chromosome 4, and 180.7 kb on chromosome 8. In addition, 85 DNA segments were identified, including some genic sequences. Contracted genes greatly outnumbered duplicated ones. Many of the contracted genes corresponded to either the same genes or genes involved in the same biological processes; this was also the case for genes involved in disease and defense.</p> <p>Conclusion</p> <p>We detected CNVs in rice by array-based comparative genomic hybridization. These CNVs contain known genes. Further discussion of CNVs is important, as they are linked to variation among rice varieties, and are likely to contribute to subspecific characteristics.</p

Variation and correlation of four cooking and eating quality indices of rice

Estimates of variation in cooking and eating quality indices, amylose content (AC), gel consistency (GC), starch final gelatinization temperature (GT), and grain elongation (GE) of 245 rice varieties from 27 countries revealed a wide variation in these characteristics. The analyses of simple correlation showed that the extent and magnitude of the correlations between possible pairs of AC, GC, GT, and GE varied greatly. The following combinations of indices were not represented: waxy (0-2% AC) and medium or hard GC, very low AC and hard GC, high GT and intermediate or high AC, high GT and medium or hard GC. These combinations were rare: hard GC with low AC, intermediate GT and waxy or very low AC. Differential grain elongation ability could exist with any level of the other three grain quality indices, but was highest among rices with intermediate AC, intermediate GT, and soft GC. The results showed the existence of the consumer preference variation in rice cooking and eating quality characteristics

A constrained multi-objective evolutionary strategy based on population state detection

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The difficulty of solving constrained multi-objective optimization problems (CMOPs) using evolutionary algorithms is to balance constraint satisfaction and objective optimization while fully considering the diversity of the solution set. Many CMOPs with disconnected feasible subregions make it difficult for algorithms to search for all feasible nondominated solutions. To address these issues, we propose a population state detection strategy (PSDS) and a restart scheme to determine whether the environmental selection strategy needs to be changed based on the situation of population. When the population converges in the feasible region, the unconstrained environmental selection allows the population to cross the current feasible region. When the population converging outside the feasible region, all constraints will be considered in the environmental selection to select the population for the feasible region. In addition, the restart scheme will use reinitialization to make the population jump out of unprofitable iterations. The proposed algorithm enhances the search ability through the detection strategy and provides more diversity by reinitializing the population. The experimental results on four constraint test suites with various features have demonstrated that the proposed algorithm had better or competitive performance against other state-of-the-art constrained multi-objective algorithms

Bacterial-Artificial-Chromosome-Based Genome Editing Methods and the Applications in Herpesvirus Research

Herpesviruses are major pathogens that infect humans and animals. Manipulating the large genome is critical for exploring the function of specific genes and studying the pathogenesis of herpesviruses and developing novel anti-viral vaccines and therapeutics. Bacterial artificial chromosome (BAC) technology significantly advanced the capacity of herpesviruses researchers to manipulate the virus genomes. In the past years, advancements in BAC-based genome manipulating and screening strategies of recombinant BACs have been achieved, which has promoted the study of the herpes virus. This review summarizes the advances in BAC-based gene editing technology and selection strategies. The merits and drawbacks of BAC-based herpesvirus genome editing methods and the application of BAC-based genome manipulation in viral research are also discussed. This review provides references relevant for researchers in selecting gene editing methods in herpes virus research. Despite the achievements in the genome manipulation of the herpes viruses, the efficiency of BAC-based genome manipulation is still not satisfactory. This review also highlights the need for developing more efficient genome-manipulating methods for herpes viruses