Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms

Decomposition-based multiobjective evolutionary algorithms (MOEAs) have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions (SFs), which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This paper is mainly devoted to presenting two new SFs and analyzing their effect in decomposition-based MOEAs. Additionally, we come up with an efficient framework for decomposition-based MOEAs based on the proposed SFs and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed SFs and algorithm

NIHBA : A network interdiction approach for metabolic engineering design

Funding Information: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) for funding project ‘Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)’. N.K. was funded by a Royal Academy of Engineering Chair in Emerging Technology award.Peer reviewe

Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation

Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. Such problems pose various challenges to evolutionary algorithms, which have popularly been used to solve complex optimisation problems, due to their dynamic nature and resource restrictions in changing environments. This paper proposes vector autoregressive evolution (VARE) consisting of vector autoregression (VAR) and environment-aware hypermutation to address environmental changes in DMO. VARE builds a VAR model that considers mutual relationship between decision variables to effectively predict the moving solutions in dynamic environments. Additionally, VARE introduces EAH to address the blindness of existing hypermutation strategies in increasing population diversity in dynamic scenarios where predictive approaches are unsuitable. A seamless integration of VAR and EAH in an environment-adaptive manner makes VARE effective to handle a wide range of dynamic environments and competitive with several popular DMO algorithms, as demonstrated in extensive experimental studies. Specially, the proposed algorithm is computationally 50 times faster than two widely-used algorithms (i.e., TrDMOEA and MOEA/D-SVR) while producing significantly better results

Analyses of clinicopathological, molecular, and prognostic associations of KRAS codon 61 and codon 146 mutations in colorectal cancer: cohort study and literature review

Background: KRAS mutations in codons 12 and 13 are established predictive biomarkers for anti-EGFR therapy in colorectal cancer. Previous studies suggest that KRAS codon 61 and 146 mutations may also predict resistance to anti-EGFR therapy in colorectal cancer. However, clinicopathological, molecular, and prognostic features of colorectal carcinoma with KRAS codon 61 or 146 mutation remain unclear. Methods: We utilized a molecular pathological epidemiology database of 1267 colon and rectal cancers in the Nurse’s Health Study and the Health Professionals Follow-up Study. We examined KRAS mutations in codons 12, 13, 61 and 146 (assessed by pyrosequencing), in relation to clinicopathological features, and tumor molecular markers, including BRAF and PIK3CA mutations, CpG island methylator phenotype (CIMP), LINE-1 methylation, and microsatellite instability (MSI). Survival analyses were performed in 1067 BRAF-wild-type cancers to avoid confounding by BRAF mutation. Cox proportional hazards models were used to compute mortality hazard ratio, adjusting for potential confounders, including disease stage, PIK3CA mutation, CIMP, LINE-1 hypomethylation, and MSI. Results: KRAS codon 61 mutations were detected in 19 cases (1.5%), and codon 146 mutations in 40 cases (3.2%). Overall KRAS mutation prevalence in colorectal cancers was 40% (=505/1267). Of interest, compared to KRAS-wild-type, overall, KRAS-mutated cancers more frequently exhibited cecal location (24% vs. 12% in KRAS-wild-type; P < 0.0001), CIMP-low (49% vs. 32% in KRAS-wild-type; P < 0.0001), and PIK3CA mutations (24% vs. 11% in KRAS-wild-type; P < 0.0001). These trends were evident irrespective of mutated codon, though statistical power was limited for codon 61 mutants. Neither KRAS codon 61 nor codon 146 mutation was significantly associated with clinical outcome or prognosis in univariate or multivariate analysis [colorectal cancer-specific mortality hazard ratio (HR) = 0.81, 95% confidence interval (CI) = 0.29-2.26 for codon 61 mutation; colorectal cancer-specific mortality HR = 0.86, 95% CI = 0.42-1.78 for codon 146 mutation]. Conclusions: Tumors with KRAS mutations in codons 61 and 146 account for an appreciable proportion (approximately 5%) of colorectal cancers, and their clinicopathological and molecular features appear generally similar to KRAS codon 12 or 13 mutated cancers. To further assess clinical utility of KRAS codon 61 and 146 testing, large-scale trials are warranted

Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons

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.Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the design of benchmark test functions of DCOPs is still insufficient. Therefore, we propose a test suite for DCOPs. A dynamic unconstrained optimization benchmark with good time-varying characteristics, called moving peaks benchmark, is chosen to be the objective function of our test suite. In addition, we design adjustable dynamic constraints, by which the size, number, and change severity of the feasible regions can be flexibly controlled. Furthermore, the performance of three dynamic constrained optimization evolutionary algorithms is tested on the proposed test suite, one of which is presented in this paper, named dynamic constrained optimization differential evolution (DyCODE). DyCODE includes three main phases: 1) the first phase intends to enter the feasible region from different directions promptly via a multi-population search strategy; 2) in the second phase, some excellent individuals chosen from the first phase form a new population to search for the optimal solution of the current environment; and 3) the third phase combines the memory individuals of the first two phases with some randomly generated individuals to re-initialize the population for the next environment. From the experiments, one can understand the strengths and weaknesses of the three compared algorithms for solving DCOPs in depth. Moreover, we also give some suggestions for researchers to apply these three algorithms on different occasions

IMM Iterated Extended Particle Filter Algorithm

In order to solve the tracking problem of radar maneuvering target in nonlinear system model and non-Gaussian noise background, this paper puts forward one interacting multiple model (IMM) iterated extended particle filter algorithm (IMM-IEHPF). The algorithm makes use of multiple modes to model the target motion form to track any maneuvering target and each mode uses iterated extended particle filter (IEHPF) to deal with the state estimation problem of nonlinear non-Gaussian system. IEHPF is an improved particle filter algorithm, which utilizes iterated extended filter (IEHF) to obtain the mean value and covariance of each particle and describes importance density function as a combination of Gaussian distribution. Then according to the function, draw particles to approximate the state posteriori density of each mode. Due to the high filter accuracy of IEHF and the adaptation of system noise with arbitrary distribution as well as strong robustness, the importance density function generated by this method is more approximate to the true sate posteriori density. Finally, a numerical example is included to illustrate the effectiveness of the proposed methods

Dynamic multi-objective optimisation based on vector autoregressive evolution

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.Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. This paper proposes vector autoregressive evolution (VARE) consisting of vector autoregression (VAR) and environment-aware hypermutation (EAH) to address environmental changes in DMO. In light of mutual dependency between decision variables in Pareto-optimal solutions, VARE builds an efficient VAR model, capturing such mutual relationship while handling dense model parameterisation with dimensionality reduction, to predict the moving solutions in dynamic environments. Additionally, VARE introduces EAH to address the blindness of existing hypermutation strategies in increasing population diversity, for scenarios where predictive approaches are unsuitable, by making hypermutation aware of the significance of environmental changes in both decision and objective spaces. A seamless integration of VAR and EAH in an environment-adaptive manner makes VARE effective to handle a variety of dynamic environments and competitive with several popular DMO algorithms, as demonstrated in extensive empirical studies. Specially, the proposed algorithm is computationally much faster than popular transfer learning based approaches while producing significantly better results

Correlation between inflammatory markers over time and disease severity in status epilepticus: a preliminary study

ObjectivesConvulsive status epilepticus (CSE) is a major subtype of status epilepticus that is known to be closely associated with systemic inflammation. Some important inflammatory biomarkers of this disorder include the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune inflammation index (SII), and pan-immune inflammation value (PIV). This study aimed to determine the NLR, PLR, MLR, SII, and PIV levels before and after treatment in adult patients with CSE and investigated the relationship of these parameters with disease severity.MethodsThis retrospective study analyzed data from 103 adult patients with CSE and 103 healthy controls. The neutrophil, monocyte, platelet, and lymphocyte counts, as well as the NLR, PLR, MLR, SII, and PIV, were compared in adult patients with CSE during acute seizures (within 2 h of admission) and after treatment relief (1–2 weeks of complete seizure control). Furthermore, multivariate linear regression analysis investigated the relationship between NLR, PLR, MLR, SII, and PIV with the Status Epilepticus Severity Score (STESS).ResultsThe data revealed significant differences (p &lt; 0.05) in neutrophils, monocytes, lymphocytes, NLR, PLR, MLR, SII, and PIV between adult patients with CSE during acute seizures and after treatment relief. The average neutrophil count was high during acute seizures in the patient group and decreased after remission. In contrast, the average lymphocyte count was lower after remission (p &lt; 0.05). Furthermore, significant differences (p &lt; 0.05) were observed in monocytes, lymphocytes, platelets, NLR, PLR, MLR, and PIV levels between adult patients with CSE after remission and the healthy control group. Multivariate linear regression analysis showed no significant correlation between NLR, PLR, MLR, SII, and PIV with STESS.ConclusionThe results of this study indicated that adult patients with CSE experienced a transient systemic inflammatory response during acute seizures, which gradually returned to baseline levels after remission. However, there was a lack of robust clinical evidence correlating the severity of adult CSE and systemic inflammatory response