Adaptation Evolution Strategy Research Articles

Dynamic optimal power flow with cross entropy covariance matrix adaption evolutionary strategy for systems with electric vehicles and renewable generators

Modern power systems which integrate renewable energy sources (RESs), such as wind or solar energy sources and plug-in electric vehicles (PEVs), need to carry out the uncertainty due to the access of demanded or injected power. Therefore, it is necessary to consider uncertainty costs in optimal power flow problems. To develop the uncertainty cost functions, log-normal probability distribution function (PDF) for solar irradiance, normal PDF for unloading and loading behavior of PEVs, and Rayleigh PDF for wind speed are considered. In this work, a dynamic optimal power flow (DOPF) model is formulated by considering the uncertainties of RESs and PEVs. A newly hybrid metaheuristic algorithm named cross entropy (CE) covariance matrix adaption evolutionary strategy (CMAES) is proposed to evaluate the DOPF problem in a modified IEEE 118-bus system. The application of adaptive step length in the CMAES method improves the local exploitation capability, while the CE method is used in the initial stage for global exploration due to its fast convergence. Simulation results indicate that the proposed technique can solve the DOPF problems with RESs and PEVs effectively and can provide high-quality solutions compared to different techniques. The conventional statistical method called ANOVA test is performed for comparative analysis of different techniques. The results of this test show the validation of CE-CMAES compared to different optimization techniques. Therefore, the proposed method could be easily and efficiently applied by power system operators in real-time optimal power flow operations.

Oil Fraction Measurement of Nonuniform Dispersed Oil–Water Two-Phase Flow Based on Ultrasonic Attenuation

Oil volume fraction determination is important for the transportation and separation of oil–water dispersed flow in petroleum industry, for which ultrasound method is suitable with the advantages of good penetration and low cost. The existing ultrasonic attenuation methods are based on homogeneous media for the determination of phase fraction without considering the effect of inhomogeneity on attenuation. In this article, the fractal approach is introduced to modify the acoustic attenuation model by characterizing the inhomogeneity of the dispersed phase in pipe flow, which establishes the relationship between multi-frequency attenuation and droplet size distribution. Then, an improved covariance matrix adaptive evolutionary strategy (CMA-ES) based on the established attenuation model is proposed to estimate oil volume fraction, which combines restart strategy with local search strategy to enhance the search performance and avoid falling into local optimum. Moreover, a demodulation method based on swept-frequency chirp is designed for the time-varying dispersed oil–water pipe flow to quickly obtain the multi-frequency attenuation. The effectiveness of the fractal-based modified attenuation model and the improved CMA-ES is verified by numerical simulations and flowing experiments. The experimental results at different oil volume fractions and flow rates indicate that the fractal-based modified attenuation model can effectively reduce the impact of nonuniform distribution of dispersed phases on the attenuation measurement, and thus effectively improve the measurement accuracy of the oil volume fraction, especially for the dispersed flow in the horizontal pipe.

Particle Size Characterization in Liquid–Solid Dispersion With Aggregates by Broadband Ultrasound Attenuation

Ultrasound attenuation methods for particle size characterization generally assume that the dispersed phases are uniformly dispersed in the continuous phase. However, the aggregation of particles in dispersions affects ultrasound attenuation, which limits the estimation accuracy of the attenuation model. A broadband ultrasound method is proposed, which utilizes a fractal approach to address the acoustic attenuation influence of particle aggregation. First, a chirp-based swept-frequency mode is designed to demodulate the ultrasonic attenuation at a low time cost to ensure the consistency of multifrequency attenuation. Next, considering the effect of particle aggregation on attenuation, the ultrasound attenuation model based on Faran scattering theory is modified by the fractal approach. Then, the inversion of particle size characterization is formulated as an optimization problem of nonlinear and nonconvex functions. An iterative optimization method is proposed to solve the ill-posed inverse problem, in which covariance matrix adaptive evolutionary strategy (CMA-ES) is developed as its inverse solver, and the fractal modified attenuation model is developed as its forward solver. Finally, the experiments were carried out with a mixture of micron-sized polystyrene particles and water. Experimental results indicate that the attenuation predicted by the proposed attenuation model is in good agreement with that measured in liquid–solid dispersion with particle aggregation, and the effectiveness of CMA-ES based on multiple distribution functions is verified for the inversion of particle size characterization.

Can Coronaviruses Steal Genes from the Host as Evidenced in Western European Hedgehogs by EriCoV Genetic Characterization?

Due to their need for living cells, viruses have developed adaptive evolutionary strategies to survive and perpetuate in reservoir hosts that play a crucial role in the ecology of emerging pathogens. Pathogenic and potentially pandemic betacoronaviruses arose in humans in 2002 (SARS-CoV, disappeared in July 2003), 2012 (MERS-CoV, still circulating in Middle East areas), and 2019 (SARS-CoV-2, causing the current global pandemic). As universally recognized, bats host ancestors of the above-mentioned zoonotic viruses. However, hedgehogs have been recently identified in Europe and Asia as possible reservoirs of MERS-CoV-like strains classified as Erinaceus coronavirus (EriCoV). To elucidate the evolution and genetics of EriCoVs, NGS (next generation sequencing) and Sanger sequencing were used to examine fecal samples collected in Northern Italy in 2018/2019 from 12 hedgehogs previously found EriCoV-positive by RT-PCR. By sequence analysis, eight complete EriCoV genomes, obtained by NGS, showed a high phylogenetic correlation with EriCoV strains previously reported in Eurasia. Interestingly, eight viral strains presented an additional ORF encoding for the CD200 ortholog located between the genes encoding for the Spike and the ORF3a proteins. The CD200 ortholog sequences were closely similar to the host CD200 protein but varying among EriCoVs. The result, confirmed by Sanger sequencing, demonstrates for the first time that CoVs can acquire host genes potentially involved in the immune-modulatory cascade and possibly enabling the virus to escape the host defence.

A tree–seed algorithm based on intelligent search mechanisms for continuous optimization

One of the recently proposed metaheuristic algorithms is tree–seed algorithm, TSA for short. TSA is developed by inspiring the relation between trees and their seeds in order to solve continuous optimization problems, and it has a simple but effective algorithmic structure. The algorithm uses two different solution generating mechanisms in order to improve balance local and global search abilities. However, when the algorithm is analyzed in detail, it is seen that there are some issues in the basic algorithm. These are (i) when trees in the stand approaches to each other, the diversification in the stand is lost, (ii) there is no mechanism to get rid of local minima for a tree, (iii) some of the fitness calculation goes to waste due to seed generation mechanism of basic TSA. In order to address these issues, four different approaches (withering process, sequential seed generation, best-based solution update rule and dimensional selection for the solution update rule) have been proposed for the basic TSA, and all these approaches have been also integrated within algorithmic framework of TSA, named new tree–seed algorithm briefly NTSA, and each of them has been used to solve 28 CEC2013 benchmark functions. In the experimental comparisons, the variants of TSA have been compared with each other, and the better algorithm, NTSA, has been compared with 17 state-of-art algorithms such as artificial bee colony, particle swarm optimization, differential evolution, genetic algorithm, covariance matrix adaptation evolutionary strategy etc. The experimental analysis and comparisons show that the NTSA shows better or similar performance than/with the compared algorithms in terms of solution quality and robustness.

Identification of miRNA Biomarkers for Diverse Cancer Types Using Statistical Learning Methods at the Whole-Genome Scale.

Genome-wide analysis of miRNA molecules can reveal important information for understanding the biology of cancer. Typically, miRNAs are used as features in statistical learning methods in order to train learning models to predict cancer. This motivates us to propose a method that integrates clustering and classification techniques for diverse cancer types with survival analysis via regression to identify miRNAs that can potentially play a crucial role in the prediction of different types of tumors. Our method has two parts. The first part is a feature selection procedure, called the stochastic covariance evolutionary strategy with forward selection (SCES-FS), which is developed by integrating stochastic neighbor embedding (SNE), the covariance matrix adaptation evolutionary strategy (CMA-ES), and classifiers, with the primary objective of selecting biomarkers. SNE is used to reorder the features by performing an implicit clustering with highly correlated neighboring features. A subset of features is selected heuristically to perform multi-class classification for diverse cancer types. In the second part of our method, the most important features identified in the first part are used to perform survival analysis via Cox regression, primarily to examine the effectiveness of the selected features. For this purpose, we have analyzed next generation sequencing data from The Cancer Genome Atlas in form of miRNA expression of 1,707 samples of 10 different cancer types and 333 normal samples. The SCES-FS method is compared with well-known feature selection methods and it is found to perform better in multi-class classification for the 17 selected miRNAs, achieving an accuracy of 96%. Moreover, the biological significance of the selected miRNAs is demonstrated with the help of network analysis, expression analysis using hierarchical clustering, KEGG pathway analysis, GO enrichment analysis, and protein-protein interaction analysis. Overall, the results indicate that the 17 selected miRNAs are associated with many key cancer regulators, such as MYC, VEGFA, AKT1, CDKN1A, RHOA, and PTEN, through their targets. Therefore the selected miRNAs can be regarded as putative biomarkers for 10 types of cancer.

Short communication: Modulation of fatty acid metabolism improves oxygen tolerance of Bifidobacterium animalis ssp. lactis Probio-M8

Bifidobacterium animalis ssp. lactis Probio-M8 is a potential probiotic strain that was isolated from human milk. Previously, we obtained an oxygen-resistant variant (Probio-M8o) of Probio-M8 by an adaptive evolution strategy. In the present study, a comparative transcriptomic analysis of Probio-M8o and Probio-M8 was carried out to reveal the cellular mechanism of the oxygen-resistant phenotype. Using RNA-seq, 210 and 217 differentially expressed genes were identified in Probio-M8o compared with Probio-M8 after oxygen exposure for 30 and 60 min, respectively. The oxygen treatment upregulated a set of genes that encoded proteins responsible for fatty acid biosynthesis. This observation was in good agreement with the composition change in fatty acids at the biochemical level. Our study showed that the oxygen-resistant phenotype could be related to adaptation of fatty acid metabolism.

Comparative Study of Heuristic Algorithms for Electrical Impedance Tomography

Based on electrical measurements from electrodes placed around the boundary of a body, electrical impedance tomography (EIT) is an imaging procedure that recovers the spatial distribution of the conductivities in the interior of a body. Recent studies have shown promising results in reconstructing EIT images using heuristic algorithms. This work presents a study of the applicability of six heuristic algorithms – firefly algorithm (FA), novel bat algorithm (NBA), genetic algorithm with new multi-parent crossover (GA-MPC), success history-based adaptive differential evolution with linear population size reduction with semiparameter adaptation hybrid with covariance matrix adaptation evolutionary strategy (LSHADE-SPACMA), ensemble sinusoidal differential covariance matrix adaptation (LSHADE-cnEpSin), and effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR) – for the EIT image reconstruction problem. These algorithms have never been employed to solve the EIT inverse problem. Series of numerical tests were carried out to compare the performance of the selected algorithms.

An improved MOEA/D algorithm with an adaptive evolutionary strategy

The Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) overcomes the limitation of evolutionary algorithm based on a Pareto dominant relationship in dealing with the problem of super multi-objective optimization and has wide application prospects, but there are also some problems, such as the lack of diversity and slow convergence speed in the later-stage evolution species. This article specifically conducts a systematic study on the population diversity of the MOEA/D algorithm and proposes three improvements: firstly, the evolutionary strategy of competition between SBX and DE operator is adopted to overcome the problem of the species diversity degradation of a single operator; secondly, an adaptive adjusting strategy of modulation probability is introduced to promote the variability of later-stage evolution species; finally, a method of double-faced mirror theory boundary optimization is used to prevent species aggregating at the boundary. The research shows that the above three improvement measures can effectively improve the population diversity of the MOEA/D algorithm. On the basis of this research, an improved MOEA/D algorithm with adaptive evolution strategy (AES-MOEA/D) is proposed. Simulation experiment indicators show that the convergence and comprehensive performance of the AES-MOEA/D algorithm are better than that of the basic MOEA/D algorithm and the other four comparison algorithms, which shows that the research on the maintenance of the diversity of the MOEA/D algorithm population helps improve the comprehensive performance of the MOEA/D algorithm.

Microbial insights into the biogeochemical features of thallium occurrence: A case study from polluted river sediments

Thallium (Tl) is a trace element with extreme toxicity. Widespread Tl pollution in riverine systems, mainly due to escalating mining and smelting activities of Tl-bearing sulfide minerals, has attracted increasing attention. Insights into the function of the microbial communities with advanced characterization tools are critical for understanding the biogeochemical cycle of Tl. Herein, microbial communities and their adaptive evolution strategies in river sediments from a representative Tl-bearing pyrite mine area in southern China were profiled via 16S rRNA gene sequence analysis and shotgun metagenomic analysis. In total, 64 phyla and 778 genera of microorganisms were observed in the studied sediments. The results showed that pH, Tl, Pb, Zn and total organic carbon (TOC) had a significant influence on microbial community structure. Some important reductive microorganisms (such as Erysipelothrix, Geobacter, desulfatiferula, desulfatihabadium and fusibacter) were involved in the biogeochemical cycle of Tl. The ruv, rec, ars and other resistance genes enhanced the tolerance of microorganisms to Tl. The study suggested that relevant C, N and S cycle genes were the main metabolic paths of microorganisms surviving in the high Tl-polluted environment. The findings were critical for establishment, operation and regulation in the microbial treatment of Tl containing or related wastewater.

A shape optimization of ϕ-shape Darrieus wind turbine under a given range of inlet wind speed

Abstract The ϕ-shape Darrieus wind turbines have great potential in application due to their omni-directionality and structural advantages. However, to achieve a higher aerodynamic performance, the design of such turbine needs attentive optimization to fit the surrounding wind variation. In this paper, a performance optimization of the shape of ϕ-shape Darrieus wind turbine with a given range of inlet wind speed is carried out. By involving a heuristic search algorithm, Covariance Matrix Adaptation Evolutionary Strategy (CMAES), into Double Multiple Streamtube model (DMST), three geometrical variables of the rotor: the equatorial radius ( R ), the ratio of radius over half-height ( β ) and the blade number ( B ) are modified according to the fitness function that was specially built to satisfy the inlet wind range requirements. Moreover, to validate the optimization output, a 3D CFD simulation is conducted as a comparison. The result shows that this program can present an entirely optimized model under the given range of inlet wind speed, with a 12.5% improved C p at the optimal velocity compared with the baseline. Verification from CFD method shows a satisfactory agreement for the optimized model compared with the DMST output, indicating that this algorithm could provide a reliable reference for the shape selection of ϕ-shape Darrieus turbines under a certain inlet wind condition.

Muscle-Synergies-Based Neuromuscular Control for Motion Learning and Generalization of a Musculoskeletal System

Owing to its potential superiorities in terms of flexibility, compliance, and robustness, the musculoskeletal robotic system has become a promising direction for next-generation robots. However, motion learning and generalization of musculoskeletal systems are still challenging problems. In this article, a muscle-synergies-based neuromuscular control is proposed. First, a new computational model of time-varying muscle synergies is constructed, which utilizes both phasic and tonic muscle synergies to characterize the basic features of muscle excitations more sufficiently. Second, a novel neuromuscular control method is proposed for realizing the motion learning and generalization of musculoskeletal systems. Therein, a radial basis function (RBF) neural network is designed to modulate muscle synergies according to different movement targets. Muscle excitations are computed with the combination of modulated muscle synergies. Covariance matrix adaptation evolutionary strategy is applied to realize the synchronous optimization of muscle synergies and the RBF neural network. In the experiment, a sophisticated musculoskeletal system learns to perform center-out reaching tasks through trial-and-error learning on a few targets. With the muscle synergies and neural modulation learned from a few targets, the musculoskeletal system can also reach many unexperienced targets. The proposed method not only improves the speed and accuracy of motion learning but also enhances motion generalization. This article also promotes the development of the musculoskeletal robotic system and the fusion of neuroscience and robotics.

A hybrid-encoding adaptive evolutionary strategy algorithm for windage alteration fault diagnosis

It is critically important that windage alteration faults (WAFs) within mine ventilation systems be quickly identified and mitigated in order to ensure a safe mine production environment. Thus, we propose a Hybrid-Encoding adaptive Evolution Strategy (ES) Algorithm to diagnose the fault’s location and volume quickly and accurately, as it combines classification and regression features. The Euclidean distance between the airflow set calculated via fault diagnosis and the airflow set obtained by the monitoring system was used as the objective function value. Six benchmark functions and one thousand six hundred tests were carried out to verify the feasibility of using Hybrid-Encoding for WAFs diagnosis. The effectiveness of adaptive ES was demonstrated by Genetic Algorithm (GA), Differential Evolution Algorithm (DEA), and Particle Swarm Optimization (PSO). The experimental results fully validate the Hybrid-Encoding adaptive ES superiority in terms of accuracy, precision, diagnostic errors, robustness, computational efficiency, and convergence speed, etc. Diagnostic accuracy and precision during field testing were both 92.5 %, and 93.75 % of the results showed relative errors of < 5 %. Thus, our proposed Hybrid-Encoding adaptive ES Algorithm meets the requirements for fault diagnosis accuracy at the mine production site.

Nonlinear process modelling using echo state networks optimised by covariance matrix adaption evolutionary strategy

Echo state networks (ESN) have been shown to be an effective alternative to conventional recurrent neural networks (RNNs) due to its fast training process and good performance in dynamic system modelling. However, the performance of ESN can be affected by the randomly generated reservoir. This paper presents nonlinear process modelling using ESN optimized by covariance matrix adaption evolutionary strategy (CMA-ES). CMA-ES is used to optimize the structural parameters of ESN: reservoir size, spectral radius, and leak rate. The proposed method is applied to three case studies: modelling a time series, modelling a conic tank, and modelling a fed-batch penicillin fermentation process. The results are compared with those from the original ESN, long short-term memory network, GA-ESN (ESN optimized by genetic algorithm), and feedforward neural networks. It is shown that the proposed method gives much better performance than the original ESN and other networks on all the three case studies.

A Novel Approach to Coordinating Green Wave System With Adaptation Evolutionary Strategy

Urban arterial traffic coordination control has attracted much attention in smart city construction process. To achieve optimal signal timing, many studies have attempted to adjust green splits of a cycle time according to the distance between road intersections. However, existing green wave traffic control systems usually require a sophisticated calculation that depend upon the stability of vehicle speed and traffic flow, which can lead to weak robustness. Therefore, this article proposes two novel approaches to control arterial traffic coordination with the help of artificial intelligence: DDPG-BAND and ES-BAND. DDPG-BAND has two stages: a coarse-tuning stage reduces the blocking coefficient, and a fine-tuning stage optimizes the traffic evaluation index. ES-BAND introduces the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), a scalable alternative to reinforcement learning, into signal timing. Different traffic variables are adopted as parameters to search for the optimal value by the CMA-ES. To evaluate the feasibility and effectiveness of our approaches, we import real traffic flow data from Zhongshan Road, Ningbo, Zhejiang Province, China, into a traffic environment simulator for training and then conduct a series of experiments. The results show that ES-BAND outperforms the traditional methods in terms of better convergence, lower journey time, fewer stops, and more throughput.

Comparative Study of Optimization Algorithms on the Design of Broadband Antennas

Broadband antennas find many applications in modern communication systems, such as Wi-Fi, 5G, and SatCom. Multiple competing optimization methods are available for the application to antenna design, while it would be preferable to know in advance if any method is superior. Here, an application of the cross-entropy method, along with particle swarm optimization and covariance matrix adaptation evolutionary strategy, to the design of broadband antennas is presented. The first example is an aperture-coupled microstrip patch antenna that has 9.5 dBi peak directivity and 53% bandwidth after optimization. It is then used as a feed in a high-gain broadband resonant cavity antenna. Using an all-dielectric superstrate with a transverse permittivity gradient, a compact thin resonant cavity antenna with a peak directivity of 19 dBi and 40% 3-dB bandwidth was designed. A comparative analysis of the cross-entropy method, particle swarm optimization, and covariance matrix adaptation evolutionary strategy applied to these two problems was carried out to provide the basis for further optimization of antennas in radio frequency and microwave frequency bands. We found that although all three methods reached a similar solution, the cross-entropy method has a speed advantage. It improves our ability to optimize existing designs and has wider applicability beyond antenna engineering.

A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.

ReaxFF Parameter Optimization with Monte-Carlo and Evolutionary Algorithms: Guidelines and Insights.

ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be optimized by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular, genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, that is, Monte-Carlo force field optimizer (MCFF) and covariance matrix adaptation evolutionary strategy (CMA-ES). In this work, CMA-ES, MCFF, and a GA method (OGOLEM) are systematically compared using three training sets from the literature. By repeating optimizations with different random seeds and initial parameter guesses, it is shown that a single optimization run with any of these methods should not be trusted blindly: nonreproducible, poor or premature convergence is a common deficiency. GA shows the smallest risk of getting trapped into a local minimum, whereas CMA-ES is capable of reaching the lowest errors for two-third of the cases, although not systematically. For each method, we provide reasonable default settings, and our analysis offers useful guidelines for their usage in future work. An important side effect impairing parameter optimization is numerical noise. A detailed analysis reveals that it can be reduced, for example, by using exclusively unambiguous geometry optimization in the training set. Even without this noise, many distinct near-optimal parameter vectors can be found, which opens new avenues for improving the training set and detecting overfitting artifacts.

Combining metabolic flux analysis and adaptive evolution to enhance lipase production in Bacillus subtilis.

Metabolic fluxes during lipase production by Bacillus subtilis CICC 20034 in synthetic medium were studied using metabolic flux analysis (MFA). The MFA showed that lipase production was dependent on, and coupled to the tributyrin uptake rate, formation of biomass, lactate, ATP, as well as amino acids from the aspartate and glutamate family. Using tributyrin as the sole carbon source, an adaptive evolution strategy was applied to increase the tributyrin uptake rate. B. subtilis SPZ1 was obtained from CICC 20034 by adaptive evolution over 1000 generations of growth-based selection. The tributyrin consumption rate of strain SPZ1 reached 0.89g/(L·h) which was 1.9-fold higher than that of the original strain. The MFA indicated that the 212% increase of tributyrin uptake flux contributed to the 556% increase of lipase flux. Consequently, the lipase activity (0.65U/mL) of strain SPZ1 was 1.9-fold higher than that of the original strain. This was the highest lipase activity obtained by fermentation in synthetic medium reported for Bacillus strains. In complex culture medium, lipase activity of SPZ1 reached 3.3U/mL.

Clinical Persistence of Chlamydia trachomatis Sexually Transmitted Strains Involves Novel Mutations in the Functional αββα Tetramer of the Tryptophan Synthase Operon.

Clinical persistence of Chlamydia trachomatis (Ct) sexually transmitted infections (STIs) is a major public health concern. In vitro persistence is known to develop through interferon gamma (IFN-γ) induction of indoleamine 2,3-dioxygenase (IDO), which catabolizes tryptophan, an essential amino acid for Ct replication. The organism can recover from persistence by synthesizing tryptophan from indole, a substrate for the enzyme tryptophan synthase. The majority of Ct strains, except for reference strain B/TW-5/OT, contain an operon comprised of α and β subunits that encode TrpA and TrpB, respectively, and form a functional αββα tetramer. However, trpA mutations in ocular Ct strains, which are responsible for the blinding eye disease known as trachoma, abrogate tryptophan synthesis from indole. We examined serial urogenital samples from a woman who had recurrent Ct infections over 4 years despite antibiotic treatment. The Ct isolates from each infection episode were genome sequenced and analyzed for phenotypic, structural, and functional characteristics. All isolates contained identical mutations in trpA and developed aberrant bodies within intracellular inclusions, visualized by transmission electron microscopy, even when supplemented with indole following IFN-γ treatment. Each isolate displayed an altered αββα structure, could not synthesize tryptophan from indole, and had significantly lower trpBA expression but higher intracellular tryptophan levels compared with those of reference Ct strain F/IC-Cal3. Our data indicate that emergent mutations in the tryptophan operon, which were previously thought to be restricted only to ocular Ct strains, likely resulted in in vivo persistence in the described patient and represents a novel host-pathogen adaptive strategy for survival.IMPORTANCEChlamydia trachomatis (Ct) is the most common sexually transmitted bacterium with more than 131 million cases occurring annually worldwide. Ct infections are often asymptomatic, persisting for many years despite treatment. In vitro recovery from persistence occurs when indole is utilized by the organism's tryptophan synthase to synthesize tryptophan, an essential amino acid for replication. Ocular but not urogenital Ct strains contain mutations in the synthase that abrogate tryptophan synthesis. Here, we discovered that the genomes of serial isolates from a woman with recurrent, treated Ct STIs over many years were identical with a novel synthase mutation. This likely allowed long-term in vivo persistence where active infection resumed only when tryptophan became available. Our findings indicate an emerging adaptive host-pathogen evolutionary strategy for survival in the urogenital tract that will prompt the field to further explore chlamydial persistence, evaluate the genetics of mutant Ct strains and fitness within the host, and their implications for disease pathogenesis.