Search results for: evolutionary analysis

Authors: Yassir AbdelRazig

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Evolutionary optimization methods such as genetic algorithms have been used extensively for the construction site layout problem. More recently, ant colony optimization algorithms, which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to benchmark combinatorial optimization problems. This paper proposes a formulation of the site layout problem in terms of a sequencing problem that is suitable for solution using an ant colony optimization algorithm. In the construction industry, site layout is a very important planning problem. The objective of site layout is to position temporary facilities both geographically and at the correct time such that the construction work can be performed satisfactorily with minimal costs and improved safety and working environment. During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the construction site layout problem. This paper proposes an ant colony optimization model for construction site layout. A simple case study for a highway project is utilized to illustrate the application of the model.

Keywords: ant colony, construction site layout, optimization, genetic algorithms

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Authors: Charalampos Saridakis, Stelios Tsafarakis

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Although environmental concern is on the rise across Europe, current market data indicate that adoption rates of environmentally friendly vehicles remain extremely low. Against this background, the aim of this paper is to a) assess preferences of European consumers for clean-fuel cars and their characteristics and b) design car lines that optimize the combination of fuel types among models in the line-up. In this direction, the authors introduce a new evolutionary mechanism and implement it to stated-preference data derived from a large-scale choice-based conjoint experiment that measures consumer preferences for various factors affecting clean-fuel vehicle (CFV) adoption. The proposed two-step methodology provides interesting insights into how new and existing fuel-types can be combined in a car line that maximizes customer satisfaction.

Keywords: clean-fuel vehicles, product line design, conjoint analysis, choice experiment, differential evolution

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Authors: Yadav Shyamjeet Maniram

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Despite reports of more than seventy million population of India affected by rare diseases, it rarely figured on the agenda of the Indian scientist and policymakers. Hitherto ignored, a fresh initiative is being attempted to establish the first national registry for rare diseases. Though there are registries for rare diseases, established by the clinicians and patient advocacy groups, they are isolated, scattered and lacks information sharing mechanism. It is the first time that there is an effort from the government of India to make an initiative on the rare disease registries, which would be more formal and systemic in nature. Since there is lack of epidemiological evidence for the rare disease in India, it is interesting to note how rare disease policy is being attempted in the vacuum of evidence required for the policy process. The objective of this study is to analyse rare disease registry creation and implementation from the parameters of evolutionary policy perspective in the absence of evidence for the policy process. This study will be exploratory and qualitative in nature, primarily based on the interviews of stakeholders involved in the rare disease registry creation and implementation. Some secondary data will include various documents related to rare disease registry. The expected outcome of this study would be on the role of stakeholders in the generation of evidence for the rare disease registry creation and implementation. This study will also try to capture negotiations and deliberations on the ethical issues in terms of data collection, preservation, and protection.

Keywords: evolutionary policy perspective, evidence for policy, rare disease policy, rare disease in India

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Authors: Halil Ibrahim Demir, Abdullah Hulusi Kokcam, Fuat Simsir, Özer Uygun

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Traditionally, three important manufacturing functions which are process planning, scheduling and due-date assignment are performed sequentially and separately. Although there are numerous works on the integration of process planning and scheduling and plenty of works focusing on scheduling with due date assignment, there are only a few works on integrated process planning, scheduling and due-date assignment. Although due-dates are determined without taking into account of weights of the customers in the literature, here weighted due-date assignment is employed to get better performance. Jobs are scheduled according to weighted earliest due date dispatching rule and due dates are determined according to some popular due date assignment methods by taking into account of the weights of each job. Simulated Annealing, Evolutionary Strategies, Random Search, hybrid of Random Search and Simulated Annealing, and hybrid of Random Search and Evolutionary Strategies, are applied as solution techniques. Three important manufacturing functions are integrated step-by-step and higher integration levels are found better. Search meta-heuristics are found to be very useful while improving performance measure.

Keywords: process planning, weighted scheduling, weighted due-date assignment, simulated annealing, evolutionary strategies, hybrid searches

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Authors: Sa’adu Mafara Abubakar, Muhammad Nuraddeen Danjuma, Adewole Tomiwa Adetunji, Richard Mundembe, Salisu Mohammed, Francis Bayo Lewu, Joseph I. Kiok

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Pearl millet is the most drought tolerant of all domesticated cereals, is cultivated extensively to feed millions of people who mainly live in hash agroclimatic zones. It serves as a major source of food for more than 40 million smallholder farmers living in the marginal agricultural lands of Northern Nigeria. Pearl millet grain is more nutritious than other cereals like maize, is also a principal source of energy, protein, vitamins, and minerals for millions of poorest people in the regions where it is cultivated. Pearl millet has recorded relatively little research attention compared with other crops and no sufficient work has analyzed its genetic diversity in north-western Nigeria. Therefore, this study was undertaken with the objectives to analyze the genetic diversity of pearl millet accessions using SSR marker and to analyze the extent of evolutionary relationship among pearl millet accessions at the molecular level. The result of the present study confirmed diversity among accessions of pearl millet in the study area. Simple Sequence Repeats (SSR) markers were used for genetic analysis and evolutionary relationship of the accessions of pearl millet. To analyze the level of genetic diversity, 8 polymorphic SSR markers were used to screen 69 accessions collected based on three maturity periods. SSR markers result reveal relationships among the accessions in terms of genetic similarities, evolutionary and ancestral origin, it also reveals a total of 53 alleles recorded with 8 microsatellites and an average of 6.875 per microsatellite, the range was from 3 to 9 alleles in PSMP2248 and PSMP2080 respectively. Moreover, both the factorial analysis and the dendrogram of phylogeny tree grouping patterns and cluster analysis were almost in agreement with each other that diversity is not clustering according to geographical patterns but, according to similarity, the result showed maximum similarity among clusters with few numbers of accessions. It has been recommended that other molecular markers should be tested in the same study area.

Keywords: pearl millet, genetic diversity, simple sequence repeat (SSR)

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Authors: Jianxin Zhu, Na Liu

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Entry strategies play a vital role in the development of new enterprises in the catering industry. Different entry strategies will have different effects on the development of new enterprise. Based on the research of scholars at home and abroad, and combining the characteristics of the catering industry, the entry strategies are divided into low-price entry strategies and high-quality entry strategies. Facing the entry of new enterprise, the strategies of incumbent enterprises are divided into response strategies and non-response strategies. This paper uses evolutionary game theory to study the strategic interaction mechanism between incumbent companies and new enterprises. When different initial values and parameter values are set, which strategy will the two-game subjects choose, respectively? Using matlab2016 for numerical simulation, the results show that the choice of strategies for new enterprise and incumbent enterprise is influenced by more than one factor, and the system has different evolution trends under different circumstances. When the parameters were set, the choice of two subjects' strategies mainly depends on the net profit between the strategies.

Keywords: catering industry, entry strategy, evolutionary game, strategic interaction mechanism

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Authors: Min Shi

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State of the art research with Artificial Neural Networks for the downscaling of General Circulation Models (GCMs) mainly uses back-propagation algorithm as a training approach. This paper introduces another training approach of ANNs, Evolutionary Algorithm. The combined algorithm names neuroevolutionary (NE) algorithm. We investigate and evaluate the use of the NE algorithms in statistical downscaling by generating temperature estimates at interior points given information from a lattice of surrounding locations. The results of our experiments indicate that NE algorithms can be efficient alternative downscaling methods for daily temperatures.

Keywords: temperature, downscaling, artificial neural networks, evolutionary algorithms

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Authors: Fariborz Ahmadi, Hamid Salehi, Khosrow Karimi

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A sensor network is set of sensor nodes that cooperate together to perform a predefined tasks. The important problem in this network is power consumption. So, in this paper one algorithm based on the eurygaster life is introduced to minimize power consumption by the nodes of these networks. In this method the search space of problem is divided into several partitions and each partition is investigated separately. The evaluation results show that our approach is more efficient in comparison to other evolutionary algorithm like genetic algorithm.

Keywords: evolutionary computation, genetic algorithm, particle swarm optimization, sensor network optimization

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Authors: Maneesh Kumar, Roshan Kamal Topno, Manas Ranjan Dikhit, Vahab Ali, Ganesh Chandra Sahoo, Bhawana, Major Madhukar, Rishikesh Kumar, Krishna Pandey, Pradeep Das

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Chandipura vesiculovirus is an emerging (-) ssRNA viral entity belonging to the genus Vesiculovirus of the family Rhabdoviridae, associated with fatal encephalitis in tropical regions. The multi-functionally active viral RNA-dependent RNA polymerase (vRdRp) that has been incorporated with conserved amino acid residues in the pathogens, assigned to synthesize distinct viral polypeptides. The lack of proofreading ability of the vRdRp produces many mutated variants. Here, we have performed the evolutionary analysis of 20 viral protein sequences of vRdRp of different strains of Chandipura vesiculovirus along with other viral species from genus Vesiculovirus inferred in MEGA6.06, employing the Neighbour-Joining method. The p-distance algorithmic method has been used to calculate the optimum tree which showed the sum of branch length of about 1.436. The percentage of replicate trees in which the associated taxa are clustered together in the bootstrap test (1000 replicates), is shown next to the branches. No mutation was observed in the Indian strains of Chandipura vesiculovirus. In vRdRp, 1230(His) and 1231(Arg) are actively participated in catalysis and, are found conserved in different strains of Chandipura vesiculovirus. Both amino acid residues were also conserved in the other viral species from genus Vesiculovirus. Many isolates exhibited maximum number of mutations in catalytic regions in strains of Chandipura vesiculovirus at position 26(Ser→Ala), 47 (Ser→Ala), 90(Ser→Tyr), 172(Gly→Ile, Val), 172(Ser→Tyr), 387(Asn→Ser), 1301(Thr→Ala), 1330(Ala→Glu), 2015(Phe→Ser) and 2065(Thr→Val) which make them variants under different tropical conditions from where they evolved. The result clarifies the actual concept of RNA evolution using vRdRp to develop as an evolutionary marker. Although, a limited number of vRdRp protein sequence similarities for Chandipura vesiculovirus and other species. This might endow with possibilities to identify the virulence level during viral multiplication in a host.

Keywords: Chandipura, (-) ssRNA, viral RNA-dependent RNA polymerase, neighbour-joining method, p-distance algorithmic, evolutionary marker

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Authors: Tahsin A. H. Nishat, Raquib Ahsan

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Sensitivity analysis of design parameters of the optimization procedure can become a significant factor while designing any structural system. The objectives of the study are to analyze the sensitivity of deck slab thickness parameter obtained from both the conventional and optimum design methodology of pre-stressed post-tensioned I-girder and deck system and to compare the relative significance of slab thickness. For analysis on conventional method, the values of 14 design parameters obtained by the conventional iterative method of design of a real-life I-girder bridge project have been considered. On the other side for analysis on optimization method, cost optimization of this system has been done using global optimization methodology 'Evolutionary Operation (EVOP)'. The problem, by which optimum values of 14 design parameters have been obtained, contains 14 explicit constraints and 46 implicit constraints. For both types of design parameters, sensitivity analysis has been conducted on deck slab thickness parameter which can become too sensitive for the obtained optimum solution. Deviations of slab thickness on both the upper and lower side of its optimum value have been considered reflecting its realistic possible ranges of variations during construction. In this procedure, the remaining parameters have been kept unchanged. For small deviations from the optimum value, compliance with the explicit and implicit constraints has been examined. Variations in the cost have also been estimated. It is obtained that without violating any constraint deck slab thickness obtained by the conventional method can be increased up to 25 mm whereas slab thickness obtained by cost optimization can be increased only up to 0.3 mm. The obtained result suggests that slab thickness becomes less sensitive in case of conventional method of design. Therefore, for realistic design purpose sensitivity should be conducted for any of the design procedure of girder and deck system.

Keywords: sensitivity analysis, optimum design, evolutionary operations, PC I-girder, deck system

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Authors: Tingyu Zhang

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The cross-basin ecological compensation mechanism is key to stimulating active participation in ecological protection across the entire basin. This study constructs an evolutionary game model of cross-basin ecological compensation in the Yangtze River Economic Belt (YREB), introducing a central government constraint and incentive mechanism (CGCIM) to explore the conditions for achieving strategies of protection and compensation that meet societal expectations. Furthermore, using a water quality-water quantity model combined with factual data from the YREB in 2020, the amount of ecological compensation is calculated. The results indicate that the stability of the evolutionary game model of the upstream and downstream governments in the YREB is closely related to the CGCIM. When the sum of the central government's reward amount to the upstream government and the penalty amount to both sides simultaneously is greater than 39.948 billion yuan, and the sum of the reward amount to the downstream government and the penalty amount to only the lower reaches is greater than 1.567 billion yuan, or when the sum of the reward amount to the downstream government and the penalty amount to both sides simultaneously is greater than 1.567 billion yuan, and the sum of the reward amount to the upstream government and the penalty amount to only the upstream government is greater than 399.48 billion yuan, the protection and compensation become the only evolutionarily stable strategy for the evolutionary game system composed of the upstream and downstream governments in the YREB. At this point, the total ecological compensation that the downstream government of the YREB should pay to the upstream government is 1.567 billion yuan, with Hunan paying 0.03 billion yuan, Hubei 2.53 billion yuan, Jiangxi 0.18 billion yuan, Anhui 1.68 billion yuan, Zhejiang 0.75 billion yuan, Jiangsu 6.57 billion yuan, and Shanghai 3.93 billion yuan. The research results can provide a reference for promoting the improvement and perfection of the cross-basin ecological compensation system in the YREB.

Keywords: ecological compensation, evolutionary game model, central government constraint and incentive mechanism, Yangtze river economic belt

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Authors: H. Bahbahani, H. Musa, F. Al Mathen

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The dromedary camels (Camelus dromedarius) are single-humped even-toed ungulates populating the African Sahara, Arabian Peninsula, and Southwest Asia. The genome of this desert-adapted species has been minimally investigated using autosomal microsatellite and mitochondrial DNA markers. In this study, the genomes of 33 dromedary camel samples from different parts of the Arabian Peninsula were sequenced using Illumina Next Generation Sequencing (NGS) platform. These data were combined with Genotyping-by-Sequencing (GBS) data from African (Sudanese) dromedaries to investigate the genomic relationship between African and Arabian Peninsula dromedary camels. Principle Component Analysis (PCA) and average genome-wide admixture analysis were be conducted on these data to tackle the objectives of these studies. Both of the two analyses conducted revealed phylogeographic distinction between these two camel populations. However, no breed-wise genetic classification has been revealed among the African (Sudanese) camel breeds. The Arabian Peninsula camel populations also show higher heterozygosity than the Sudanese camels. The results of this study explain the evolutionary history and migration of African dromedary camels from their center of domestication in the southern Arabian Peninsula. These outputs help scientists to further understand the evolutionary history of dromedary camels, which might impact in conserving the favorable genetic of this species.

Keywords: dromedary, genotyping-by-sequencing, Arabian Peninsula, Sudan

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Authors: Wei Tian, Jie Liang, Hammad Naveed

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Beta-barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and bacterial virulence. In addition, beta-barrel membrane proteins increasingly serve as scaffolds for bacterial surface display and nanopore-based DNA sequencing. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank and computational methods can help to understand their biophysical principles. We have developed a novel computational method to predict the 3D structure of beta-barrel membrane proteins using evolutionary coupling (EC) constraints and a reduced state space. Combined with an empirical potential function, we can successfully predict strand register at > 80% accuracy for a set of 49 non-homologous proteins with known structures. This is a significant improvement from previous results using EC alone (44%) and using empirical potential function alone (73%). Our method is general and can be applied to genome-wide structural prediction.

Keywords: beta-barrel membrane proteins, structure prediction, evolutionary constraints, reduced state space

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Authors: Muhammad Umar Kiani, Muhammad Shahbaz

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Performance of any vehicle can be predicted by its design/modeling and optimization. Design optimization leads to efficient performance. Followed by horizontal launch, the air launch re-entry vehicle undergoes a launch maneuver by introducing a carefully selected angle of attack profile. This angle of attack profile is the basic element to complete a specified mission. Flight program of said vehicle is optimized under the constraints of the maximum allowed angle of attack, lateral and axial loads and with the objective of reaching maximum altitude. The main focus of this study is the endo-atmospheric phase of the ascent trajectory. A three degrees of freedom trajectory model is simulated in MATLAB. The optimization process uses evolutionary algorithm, because of its robustness and efficient capacity to explore the design space in search of the global optimum. Evolutionary Algorithm based trajectory optimization also offers the added benefit of being a generalized method that may work with continuous, discontinuous, linear, and non-linear performance matrix. It also eliminates the requirement of a starting solution. Optimization is particularly beneficial to achieve maximum advantage without increasing the computational cost and affecting the output of the system. For the case of launch vehicles we are immensely anxious to achieve maximum performance and efficiency under different constraints. In a launch vehicle, flight program means the prescribed variation of vehicle pitching angle during the flight which has substantial influence reachable altitude and accuracy of orbit insertion and aerodynamic loading. Results reveal that the angle of attack profile significantly affects the performance of the vehicle.

Keywords: endo-atmospheric, evolutionary algorithm, efficient performance, optimization process

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Authors: Vahid Rashtchi, Ashkan Pirooz

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This paper applies the Imperialist Competitive Algorithm (ICA) to find the optimal place and size of Static Compensator (STATCOM) in power systems. The output of the algorithm is a two dimensional array which indicates the best bus number and STATCOM's optimal size that minimizes all bus voltage deviations from their nominal value. Simulations are performed on IEEE 5, 14, and 30 bus test systems. Also some comparisons have been done between ICA and the famous Particle Swarm Optimization (PSO) algorithm. Results show that how this method can be considered as one of the most precise evolutionary methods for the use of optimum compensator placement in electrical grids.

Keywords: evolutionary computation, imperialist competitive algorithm, power systems compensation, static compensators, voltage profile

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Authors: Halima Adel Halim Shnishah, David Mulvaney

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Applying traditional symmetric cryptography algorithms while computing encryption and decryption provides immunity to secret keys against different attacks. One of the popular techniques generating automated secret keys is evolutionary computing by using Eureqa API tool, which got attention in 2013. In this paper, we are generating automated secret keys for image encryption and decryption using Eureqa API (tool which is used in evolutionary computing technique). Eureqa API models pseudo-random input data obtained from a suitable source to generate secret keys. The validation of generated secret keys is investigated by performing various statistical tests (histogram, chi-square, correlation of two adjacent pixels, correlation between original and encrypted images, entropy and key sensitivity). Experimental results obtained from methods including histogram analysis, correlation coefficient, entropy and key sensitivity, show that the proposed image encryption algorithms are secure and reliable, with the potential to be adapted for secure image communication applications.

Keywords: image encryption algorithms, Eureqa, statistical measurements, automated key generation

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Authors: Rahul Paul, Kedar Nath Das

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The paradigm of Reinforcement Learning (RL) has become prominent in training intelligent agents to make decisions in environments that are both dynamic and uncertain. The primary objective of RL is to optimize the policy of an agent in order to maximize the cumulative reward it receives throughout a given period. Nevertheless, the process of optimization presents notable difficulties as a result of the inherent trade-off between exploration and exploitation, the presence of extensive state-action spaces, and the intricate nature of the dynamics involved. Evolutionary Optimization Methods (EOMs) have garnered considerable attention as a supplementary approach to tackle these challenges, providing distinct capabilities for optimizing RL policies and value functions. The ongoing advancement of research in both RL and EOMs presents an opportunity for significant advancements in autonomous decision-making systems. The convergence of these two fields has the potential to have a transformative impact on various domains of artificial intelligence (AI) applications. This article highlights the considerable influence of EOMs in enhancing the capabilities of RL. Taking advantage of evolutionary principles enables RL algorithms to effectively traverse extensive action spaces and discover optimal solutions within intricate environments. Moreover, this paper emphasizes the practical implementations of EOMs in the field of RL, specifically in areas such as robotic control, autonomous systems, inventory problems, and multi-agent scenarios. The article highlights the utilization of EOMs in facilitating RL agents to effectively adapt, evolve, and uncover proficient strategies for complex tasks that may pose challenges for conventional RL approaches.

Keywords: machine learning, reinforcement learning, loss function, optimization techniques, evolutionary optimization methods

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Authors: Kostas Metaxiotis, Kostas Liagkouras

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This study presents an Expert System specially designed to be used with Multiobjective Evolutionary Algorithms (MOEAs) for the solution of the portfolio selection problem. The validation of the proposed hybrid System is done by using data sets from Hang Seng 31 in Hong Kong, DAX 100 in Germany and FTSE 100 in UK. The performance of the proposed system is assessed in comparison with the Non-dominated Sorting Genetic Algorithm II (NSGAII). The evaluation of the performance is based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it. The results show that the proposed hybrid system is efficient for the solution of this kind of problems.

Keywords: expert systems, multi-objective optimization, evolutionary algorithms, portfolio selection

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Authors: Mohamed Amine Ben Abdallah, Imed Khemili, Nizar Aifaoui

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This work denotes an insight into dynamic synthesis of multibody systems. A set of mechanism parameters design variable are synthetized based on a desired mechanism response, such as, velocity, acceleration and bodies deformations. Moreover, knowing the work space, for a robot, and mechanism response allow defining optimal parameters mechanism handling with the desired target response. To this end, evolutionary genetic algorithm has been deployed. A demonstrative example for imperfect mechanism has been treated, mainly, a slider crank mechanism with a flexible connecting rod. The transversal deflection of the connecting rod has been chosen as response to identify the mechanism design parameters.

Keywords: dynamic response, evolutionary genetic algorithm, flexible bodies, optimization

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Authors: Emmanuel Barrantes Chaves, Ernesto Rivera Alvarado

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This paper presents a method to enable agents for the Terraria game to evaluate algorithms commonly used in general video game artificial intelligence competitions. The usage of the ‘You Only Look Once’ model in the first layer of the process obtains information from the screen, translating this information into a video game description language known as “Video Game Description Language”; the agents take that as input to make decisions. For this, the state-of-the-art algorithms were tested and compared; Monte Carlo Tree Search and Rolling Horizon Evolutionary; in this case, Rolling Horizon Evolutionary shows a better performance. This approach’s main advantage is that a VGDL beforehand is unnecessary. It will be built on the fly and opens the road for using more games as a framework for AI.

Keywords: AI, MCTS, RHEA, Terraria, VGDL, YOLOv5

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Authors: Sofia Barbosa, Mariana Pinto, José António Almeida, Edgar Carvalho, Catarina Diamantino

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The aim of this work was to test a methodology able to generate spatial-temporal maps that can synthesize simultaneously the trends of distinct hydrochemical indicators in an old radium-uranium tailings dam deposit. Multidimensionality reduction derived from principal component analysis and subsequent data aggregation derived from clustering analysis allow to identify distinct hydrochemical behavioural profiles and to generate synthetic evolutionary hydrochemical maps.

Keywords: Contamination plume migration, K-means of PCA scores, groundwater and mine water monitoring, spatial-temporal hydrochemical trends

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Authors: Agostinho Antunes

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The completion of the human genome sequencing in 2003 opened a new perspective into the importance of whole genome sequencing projects, and currently multiple species are having their genomes completed sequenced, from simple organisms, such as bacteria, to more complex taxa, such as mammals. This voluminous sequencing data generated across multiple organisms provides also the framework to better understand the genetic makeup of such species and related ones, allowing to explore the genetic changes underlining the evolution of diverse phenotypic traits. Here, recent results from our group retrieved from comparative evolutionary genomic analyses of selected marine animal species will be considered to exemplify how gene novelty and gene enhancement by positive selection might have been determinant in the success of adaptive radiations into diverse habitats and lifestyles.

Keywords: marine genomics, evolutionary bioinformatics, human genome sequencing, genomic analyses

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Authors: Nazish Badar

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In early 2009, Pandemic type A (H1N1) Influenza virus emerged globally. Since then, it has continued circulation causing considerable morbidity and mortality. The purpose of this study was to evaluate the evolutionary changes in Influenza A (H1N1) pdm09 viruses from 2009-15 and their relevance with the current vaccine viruses. Methods: Respiratory specimens were collected with influenza-like illness and Severe Acute Respiratory Illness. Samples were processed according to CDC protocol. Sequencing and phylogenetic analysis of Haemagglutinin (HA) and neuraminidase (NA) genes was carried out comparing representative isolates from Pakistan viruses. Results: Between Jan2009 - Feb 2016, 1870 (13.2%) samples were positive for influenza A out of 14086. During the pandemic period (2009–10), Influenza A/ H1N1pdm 09 was the dominant strain with 366 (45%) of total influenza positives. In the post-pandemic period (2011–2016), a total of 1066 (59.6%) cases were positive Influenza A/ H1N1pdm 09 with co-circulation of different Influenza A subtypes. Overall, the Pakistan A(H1N1) pdm09 viruses grouped in two genetic clades. Influenza A(H1N1)pdm09 viruses only ascribed to Clade 7 during the pandemic period whereas viruses belong to clade 7 (2011) and clade 6B (2015) during the post-pandemic years. Amino acid analysis of the HA gene revealed mutations at positions S220T, I338V and P100S specially associated with outbreaks in all the analyzed strains. Sequence analyses of post-pandemic A(H1N1)pdm09 viruses showed additional substitutions at antigenic sites; S179N,K180Q (SA), D185N, D239G (CA), S202A (SB) and at receptor binding sites; A13T, S200P when compared with pandemic period. Substitution at Genetic markers; A273T (69%), S200P/T (15%) and D239G (7.6%) associated with severity and E391K (69%) associated with virulence was identified in viruses isolated during 2015. Analysis of NA gene revealed outbreak markers; V106I (23%) among pandemic and N248D (100%) during post-pandemic Pakistan viruses. Additional N-Glycosylation site; HA S179N (23%), NA I23T(7.6%) and N44S (77%) in place of N386K(77%) were only found in post-pandemic viruses. All isolates showed histidine (H) at position 275 in NA indicating sensitivity to neuraminidase inhibitors. Conclusion: This study shows that the Influenza A(H1N1)pdm09 viruses from Pakistan clustered into two genetic clades, with co-circulation of some variants. Certain key substitutions in the receptor binding site and few changes indicative of virulence were also detected in post-pandemic strains. Therefore, it is imperative to continue monitoring of the viruses for early identification of potential variants of high virulence or emergence of drug-resistant variants.

Keywords: Influenza A (H1N1) pdm09, evolutionary analysis, post pandemic period, Pakistan

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Authors: Martin Pellon Consunji

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Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

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Authors: Piyush Bajpai, Sneha Pandey

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Mumbai’s Dhobighat, 150 year old largest open laundry in the world, is the true live-work place and only source of income for some of Mumbai’s highest density ‘urban poor’ residents. The regeneration of Dhobighat, due to its ultra prime location and complex socio-political culture has been a complex issue. This once flourishing urban industrial core has been degrading for the past several decades mainly due to the decline of the open laundry business, the site’s over burdened infrastructure and conflicting socio-political and economic forces. The phenomena of ‘exaptation’ or ‘co-option’ has been observed by evolutionary biologists as a process responsible for producing highly tenacious and resilient offsprings within a species. The reddish egret uses its wings to cast shadow in shallow waters to attract small fish and hunt them. An unrelated feature used opportunistically to produce a very favorable result. How can this idea of co-option be applied to resolve the complex issue of Dhobighat’s regeneration? Our paper proposes a new methodology/approach for the regeneration of Dhobighat through the lens of evolutionary biology. Forces and systems (social, political, economic, cultural and ecological) that seem conflicting or unrelated by nature are opportunistically transformed into symbiotic and complimentary relationships that produce an inclusive, resilient and holistic solution for the regeneration of Dhobighat.

Keywords: urban regeneration, exaptation, resilience, Dhobighat, Mumbai

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Authors: Xu LiYun, Briand Florent, Fan GuoLiang

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The loop-layout design problem (LLDP) aims at optimizing the sequence of positioning of the machines around the cyclic production line. Traffic congestion is the usual criteria to minimize in this type of problem, i.e. the number of additional cycles spent by each part in the network until the completion of its required routing sequence of machines. This paper aims at applying several improvements mechanisms such as a positioned-based crossover operator for the Genetic Algorithm (GA) called a Two Points Crossover (TPC) and an offspring selection process. The performance of the improved GA is measured using well-known examples from literature and compared to other evolutionary algorithms. Good results show that GA can still be competitive for this type of problem against more recent evolutionary algorithms.

Keywords: crossover, genetic algorithm, layout design problem, loop-layout, manufacturing optimization

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Authors: Ahmed E. Hodaib, Mohamed A. Hashem

Abstract:

In engineering applications, a design has to be as fully perfect as possible in some defined case. The designer has to overcome many challenges in order to reach the optimal solution to a specific problem. This process is called optimization. Generally, there is always a function called “objective function” that is required to be maximized or minimized by choosing input parameters called “degrees of freedom” within an allowed domain called “search space” and computing the values of the objective function for these input values. It becomes more complex when we have more than one objective for our design. As an example for Multi-Objective Optimization Problem (MOP): A structural design that aims to minimize weight and maximize strength. In such case, the Pareto Optimal Frontier (POF) is used, which is a curve plotting two objective functions for the best cases. At this point, a designer should make a decision to choose the point on the curve. Engineers use algorithms or iterative methods for optimization. In this paper, we will discuss the Evolutionary Algorithms (EA) which are widely used with Multi-objective Optimization Problems due to their robustness, simplicity, suitability to be coupled and to be parallelized. Evolutionary algorithms are developed to guarantee the convergence to an optimal solution. An EA uses mechanisms inspired by Darwinian evolution principles. Technically, they belong to the family of trial and error problem solvers and can be considered global optimization methods with a stochastic optimization character. The optimization is initialized by picking random solutions from the search space and then the solution progresses towards the optimal point by using operators such as Selection, Combination, Cross-over and/or Mutation. These operators are applied to the old solutions “parents” so that new sets of design variables called “children” appear. The process is repeated until the optimal solution to the problem is reached. Reliable and robust computational fluid dynamics solvers are nowadays commonly utilized in the design and analyses of various engineering systems, such as aircraft, turbo-machinery, and auto-motives. Coupling of Computational Fluid Dynamics “CFD” and Multi-Objective Evolutionary Algorithms “MOEA” has become substantial in aerospace engineering applications, such as in aerodynamic shape optimization and advanced turbo-machinery design.

Keywords: mathematical optimization, multi-objective evolutionary algorithms "MOEA", computational fluid dynamics "CFD", aerodynamic shape optimization

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Authors: Ron Cottam, Nils Langloh, Willy Ranson, Roger Vounckx

Abstract:

We start by formally describing the requirements for environmental-reaction survival computation in a natural temporally-demanding medium, and develop this into a more general model of the evolutionary context as a computational machine. The effect of this development is to replace deterministic logic by a modified form which exhibits a continuous range of dimensional fractal diffuseness between the isolation of perfectly ordered localization and the extended communication associated with nonlocality as represented by pure causal chaos. We investigate the appearance of life and consciousness in the derived general model, and propose a representation of Nature within which all localizations have the character of quasi-quantal entities. We compare our conclusions with Heisenberg’s uncertainty principle and nonlocal teleportation, and maintain that computability is the principal influence on evolution in the model we propose.

Keywords: computability, evolution, life, localization, modeling, nonlocality

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Authors: S. Yaman, S. Rostami

Abstract:

In this study, a black box modeling of the coupled-tank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutionary computation method, genetic algorithm. All tuned controllers are applied to the fuzzy model of the coupled-tank experimental setup and analyzed under the different reference input values. According to the results, it is seen that function tuner method demonstrates better robust control performance and guarantees the closed loop stability.

Keywords: function tuner method (FTM), fuzzy modeling, fuzzy PID controller, genetic algorithm (GA)

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Authors: Lydia Tan, Philippe Lemey, Lieselot Houspie, Marco Viveen, Darren Martin, Frank Coenjaerts

Abstract:

Introduction: Human respiratory syncytial virus (hRSV) is the leading cause of severe respiratory tract infections in infants under the age of two. Genomic substitutions and related evolutionary dynamics of hRSV are of great influence on virus transmission behavior. The evolutionary patterns formed are due to a precarious interplay between the host immune response and RSV, thereby selecting the most viable and less immunogenic strains. Studying genomic profiles can teach us which genes and consequent proteins play an important role in RSV survival and transmission dynamics. Study design: In this study, genetic diversity and evolutionary rate analysis were conducted on 36 RSV subgroup B whole genome sequences and 37 subgroup A genome sequences. Clinical RSV isolates were obtained from nasopharyngeal aspirates and swabs of children between 2 weeks and 5 years old of age. These strains, collected during epidemic seasons from 2001 to 2011 in the Netherlands and Belgium by either conventional or 454-sequencing. Sequences were analyzed for genetic diversity, recombination events, synonymous/non-synonymous substitution ratios, epistasis, and translational consequences of mutations were mapped to known 3D protein structures. We used Bayesian statistical inference to estimate the rate of RSV genome evolution and the rate of variability across the genome. Results: The A and B profiles were described in detail and compared to each other. Overall, the majority of the whole RSV genome is highly conserved among all strains. The attachment protein G was the most variable protein and its gene had, similar to the non-coding regions in RSV, more elevated (two-fold) substitution rates than other genes. In addition, the G gene has been identified as the major target for diversifying selection. Overall, less gene and protein variability was found within RSV-B compared to RSV-A and most protein variation between the subgroups was found in the F, G, SH and M2-2 proteins. For the F protein mutations and correlated amino acid changes are largely located in the F2 ligand-binding domain. The small hydrophobic phosphoprotein and nucleoprotein are the most conserved proteins. The evolutionary rates were similar in both subgroups (A: 6.47E-04, B: 7.76E-04 substitution/site/yr), but estimates of the time to the most recent common ancestor were much lower for RSV-B (B: 19, A: 46.8 yrs), indicating that there is more turnover in this subgroup. Conclusion: This study provides a detailed description of whole RSV genome mutations, the effect on translation products and the first estimate of the RSV genome evolution tempo. The immunogenic G protein seems to require high substitution rates in order to select less immunogenic strains and other conserved proteins are most likely essential to preserve RSV viability. The resulting G gene variability makes its protein a less interesting target for RSV intervention methods. The more conserved RSV F protein with less antigenic epitope shedding is, therefore, more suitable for developing therapeutic strategies or vaccines.

Keywords: drug target selection, epidemiology, respiratory syncytial virus, RSV

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