A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

Hybrid Selection Based Multi/Many-Objective Evolutionary Algorithm

In the last decade, numerous Multi/Many-Objective Evolutionary Algorithms (MOEAs) have been proposed to handle Multi/Many-Objective Problems (MOPs) with challenges such as discontinuous Pareto Front (PF), degenerate PF, etc. MOEAs in the literature can be broadly divided into three categories based on the selection strategy employed such as dominance, decomposition, and indicator-based MOEAs. Each category of MOEAs have their advantages and disadvantages when solving MOPs with diverse characteristics. In this work, we propose a Hybrid Selection based MOEA, referred to as HS-MOEA, which is a simple yet effective hybridization of dominance, decomposition and indicator-based concepts. In other words, we propose a new environmental selection strategy where the Pareto-dominance, reference vectors and an indicator are combined to effectively balance the diversity and convergence properties of MOEA during the evolution. The superior performance of HS-MOEA compared to the state-of-the-art MOEAs is demonstrated through experimental simulations on DTLZ and WFG test suites with up to 10 objectives.

Behaviors of Homologous Antibiotic Resistance Genes in a Cephalosporin WWTP, Subsequent WWTP and the Receiving River

High concentrations of antibiotics in antibiotic production wastewater can cause the widespread transmission of antibiotic resistance genes (ARGs). Here, we collected a set of time series samples from a cephalosporin production wastewater treatment plant (X-WWTP), the subsequent municipal WWTP (Y-WWTP) and the receiving stream. Using a functional gene microarray, GeoChip 5.0, which contains multiple homologous probes for 18 ARG and 13 antibiotic metabolism gene (AMG) families, we found that more than 50% of homologous probes for 20 gene families showed a relative abundance higher in X-WWTP, while only 10–20% showed lower relative abundance. The different response patterns of homologous ARG (hARGs) within the same ARG family imply environmental selection pressures are only responsible for the ARG enrichment and spread of some specific instead of all ARG-containing microorganisms, which contradicted the traditionally held belief that environmental selection pressures, especially antibiotic concentration, select for all ARG-containing microorganisms thereby selecting different hARGs in the same ARG family in an undifferentiated way. Network results imply that hARGs from three β_lactamase families enriched under the selection pressure of high cephalosporin antibiotic concentrations in X-WWTP formed positively correlated homologous ARG clusters (pohARGCs). The pohARGCs were also enhanced in the sediment of the receiving stream. The enrichment of hARGs from three β_lactamase families was likely through microorganisms belonging to the Betaproteobacteria genus.

Leaf Trait Networks Based on Global Data: Representing Variation and Adaptation in Plants

The interdependence of multiple traits allows plants to perform multiple functions. Acquiring an accurate representation of the interdependence of plant traits could advance our understanding of the adaptative strategies of plants. However, few studies focus on complex relationships among multiple traits. Here, we proposed use of leaf trait networks (LTNs) to capture the complex relationships among traits, allowing us to visualize all relationships and quantify how they differ through network parameters. We established LTNs using six leaf economic traits. It showed that significant differences in LTNs of different life forms and growth forms. The trait relationships of broad-leaved trees were tighter than conifers; thus, broad-leaved trees could be more efficient than conifers. The trait relationships of shrubs were tighter than trees because shrubs require multiple traits to co-operate efficiently to perform multiple functions for thriving in limited resources. Furthermore, leaf nitrogen concentration and life span had the highest centrality in LTNs; consequently, the environmental selection of these two traits might impact the whole phenotype. In conclusion, LTNs are useful tools for identifying key traits and quantifying the interdependence of multiple traits.

Deciphering Evolutionary Dynamics of WRKY I Genes in Rosaceae Species

WRKY transcription factors participate in various regulation processes at different developmental stages in higher plants. Here, 98 WRKY I genes were identified in seven Rosaceae species. The WRKY I genes are highly enriched in some subgroups and are selectively expanded in Chinese pear [Pyrus bretschneideri (P. bretschneideri)] and apple [Malus domestica (M. domestica)]. By searching for intra-species gene microsynteny, we found the majority of chromosomal segments for WRKY I-containing segments in both P. bretschneideri and M. domestica genomes, while paired segments were hardly identified in the other five genomes. Furthermore, we analyzed the environmental selection pressure of duplicated WRKY I gene pairs, which indicated that the strong purifying selection for WRKY domains may contribute to the stability of its structure and function. The expression patterns of duplication PbWRKY genes revealed that functional redundancy for some of these genes was derived from common ancestry and neo-functionalization or sub-functionalization for some of them. This study traces the evolution of WRKY I genes in Rosaceae genomes and lays the foundation for functional studies of these genes in the future. Our results also show that the rates of gene loss and gain in different Rosaceae genomes are far from equilibrium.

Taxonomic Dependency of Beta Diversity for Bacteria, Archaea and Fungi in a Semi-Arid Lake

Microbial beta diversity has been recently studied along the water depth in aquatic ecosystems, however its turnover and nestedness components remain elusive especially for multiple taxonomic groups. Based on the beta diversity partitioning developed by Baselga and Local Contributions to Beta Diversity (LCBD) partitioning by Legendre, we examined the water-depth variations in beta diversity components of bacteria, archaea and fungi in surface sediments of Hulun Lake, a semi-arid lake in northern China, and further explored the relative importance of environmental drivers underlying their patterns. We found that the relative abundances of Proteobacteria, Chloroflexi, Euryarchaeota and Rozellomycota increased towards deep water, while Acidobacteria, Parvarchaeota and Chytridiomycota decreased. For bacteria and archaea, there were significant (P < 0.05) decreasing water-depth patterns for LCBD and LCBDRepl (i.e., species replacement), while increasing patterns for total beta diversity and turnover, implying that total beta diversity and LCBD were dominated by species turnover or LCBDRepl. Further, bacteria showed a strong correlation with archaea regarding LCBD, total beta diversity and turnover. Such parallel patterns among bacteria and archaea were underpinned by similar ecological processes like environmental selection. Total beta diversity and turnover were largely affected by sediment total nitrogen, while LCBD and LCBDRepl were mainly constrained by water NO2−-N and NO3−-N. For fungal community variation, no significant patterns were observed, which may be due to different drivers like water nitrogen or phosphorus. Taken together, our findings provide compelling evidences for disentangling the underlying mechanisms of community variation in multiple aquatic microbial taxonomic groups.

Disentangling environmental effects in microbial association networks

Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges—87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

A supergene underlies linked variation in color and morphology in a Holarctic songbird

The genetic architecture of a phenotype can have considerable effects on the evolution of a trait or species. Characterizing genetic architecture provides insight into the complexity of a given phenotype and, potentially, the role of the phenotype in evolutionary processes like speciation. We use genome sequences to investigate the genetic basis of phenotypic variation in redpoll finches (Acanthis spp.). We demonstrate that variation in redpoll phenotype is broadly controlled by a ~55-Mb chromosomal inversion. Within this inversion, we find multiple candidate genes related to melanogenesis, carotenoid coloration, and bill shape, suggesting the inversion acts as a supergene controlling multiple linked traits. A latitudinal gradient in ecotype distribution suggests supergene driven variation in color and bill morphology are likely under environmental selection, maintaining supergene haplotypes as a balanced polymorphism. Our results provide a mechanism for the maintenance of ecotype variation in redpolls despite a genome largely homogenized by gene flow.

A Mating Selection Based on Modified Strengthened Dominance Relation for NSGA-III

In multi/many-objective evolutionary algorithms (MOEAs), to alleviate the degraded convergence pressure of Pareto dominance with the increase in the number of objectives, numerous modified dominance relationships were proposed. Recently, the strengthened dominance relation (SDR) has been proposed, where the dominance area of a solution is determined by convergence degree and niche size (θ¯). Later, in controlled SDR (CSDR), θ¯ and an additional parameter (k) associated with the convergence degree are dynamically adjusted depending on the iteration count. Depending on the problem characteristics and the distribution of the current population, different situations require different values of k, rendering the linear reduction of k based on the generation count ineffective. This is because a particular value of k is expected to bias the dominance relationship towards a particular region on the Pareto front (PF). In addition, due to the same reason, using SDR or CSDR in the environmental selection cannot preserve the diversity of solutions required to cover the entire PF. Therefore, we propose an MOEA, referred to as NSGA-III*, where (1) a modified SDR (MSDR)-based mating selection with an adaptive ensemble of parameter k would prioritize parents from specific sections of the PF depending on k, and (2) the traditional weight vector and non-dominated sorting-based environmental selection of NSGA-III would protect the solutions corresponding to the entire PF. The performance of NSGA-III* is favourably compared with state-of-the-art MOEAs on DTLZ and WFG test suites with up to 10 objectives.