Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization problems.The past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer simulations.Moreover,it is hard to traverse the huge search space within reasonable resource as problem dimension increases.Traditional evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory results.To reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent years.Yet there lacks a thorough review of the state of the art in this specific and important area.This paper provides a comprehensive survey of these evolutionary algorithms for HEPs.We start with a brief introduction to the research status and the basic concepts of HEPs.Then,we present surrogate-assisted evolutionary algorithms for HEPs from four main aspects.We also give comparative results of some representative algorithms and application examples.Finally,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

Integrating high-volume molecular and morphological data into the evolutionary studies of Allium

The genus Allium(Amaryllidaceae),which includes economically important plants such as onions,garlic,and leeks,is one of the most species-rich and diverse genera of monocotyledon plants in the Northern Hemisphere(Govaerts et al.,2021),with approximately 1000 species.The evolution of Allium is characterized by ecological diversification,with most species preferring open.

Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different updating mechanisms.For this reason,we consider two different aspiration-driven updating mechanisms in structured populations:satisfied-stay unsatisfied shift(SSUS)and satisfied-cooperate unsatisfied defect(SCUD).To simulate the game player’s learning process,this paper improves the particle swarm optimization algorithm,which will be used to simulate the game player’s strategy selection,i.e.,population particle swarm optimization(PPSO)algorithms.We find that in the prisoner’s dilemma,the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge.In contrast,SCUD conditions that promote the evolution of cooperation enable cooperation to emerge.In addition,the invasion of SCUD individuals helps promote cooperation among SSUS individuals.Simulated by the PPSO algorithm,the theoretical approximation results are found to be consistent with the trend of change in the simulation results.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Historical biogeography and evolutionary diversification of Lilium(Liliaceae): New insights from plastome phylogenomics

Here, we infer the historical biogeography and evolutionary diversification of the genus Lilium. For this purpose, we used the complete plastomes of 64 currently accepted species in the genus Lilium(14plastomes were newly sequenced) to recover the phylogenetic backbone of the genus and a timecalibrated phylogenetic framework to estimate biogeographical history scenarios and evolutionary diversification rates of Lilium. Our results suggest that ancient climatic changes and geological tectonic activities jointly shaped the distribution range and drove evolutionary radiation of Lilium, including the Middle Miocene Climate Optimum(MMCO), the late Miocene global cooling, as well as the successive uplift of the Qinghai-Tibet Plateau(QTP) and the strengthening of the monsoon climate in East Asia during the late Miocene and the Pliocene. This case study suggests that the unique geological and climatic events in the Neogene of East Asia, in particular the uplift of QTP and the enhancement of monsoonal climate, may have played an essential role in formation of uneven distribution of plant diversity in the Northern Hemisphere.

Clinical risk factors for preterm birth and evaluating maternal psychology in the postpartum period

BACKGROUND Although the specific pathogenesis of preterm birth(PTB)has not been thoroughly clarified,it is known to be related to various factors,such as pregnancy complications,maternal socioeconomic factors,lifestyle habits,reproductive history,environmental and psychological factors,prenatal care,and nutritional status.PTB has serious implications for newborns and families and is associated with high mortality and complications.Therefore,the prediction of PTB risk can facilitate early intervention and reduce its resultant adverse consequences.AIM To analyze the risk factors for PTB to establish a PTB risk prediction model and to assess postpartum anxiety and depression in mothers.METHODS A retrospective analysis of 648 consecutive parturients who delivered at Shenzhen Bao’an District Songgang People’s Hospital between January 2019 and January 2022 was performed.According to the diagnostic criteria for premature infants,the parturients were divided into a PTB group(n=60)and a full-term(FT)group(n=588).Puerperae were assessed by the Self-rating Anxiety Scale(SAS)and Self rating Depression Scale(SDS),based on which the mothers with anxiety and depression symptoms were screened for further analysis.The factors affecting PTB were analyzed by univariate analysis,and the related risk factors were identified by logistic regression.RESULTS According to univariate analysis,the PTB group was older than the FT group,with a smaller weight change and greater proportions of women who underwent artificial insemination and had gestational diabetes mellitus(P<0.05).In addition,greater proportions of women with reproductive tract infections and greater white blood cell(WBC)counts(P<0.05),shorter cervical lengths in the second trimester and lower neutrophil percentages(P<0.001)were detected in the PTB group than in the FT group.The PTB group exhibited higher postpartum SAS and SDS scores than did the FT group(P<0.0001),with a higher number of mothers experiencing anxiety and depression(P<0.001).Multivariate logistic regression analysis revealed that a greater maternal weight change,the presence of gestational diabetes mellitus,a shorter cervical length in the second trimester,a greater WBC count,and the presence of maternal anxiety and depression were risk factors for PTB(P<0.01).Moreover,the risk score of the FT group was lower than that of the PTB group,and the area under the curve of the risk score for predicting PTB was greater than 0.9.CONCLUSION This study highlights the complex interplay between postpartum anxiety and PTB,where maternal anxiety may be a potential risk factor for PTB,with PTB potentially increasing the incidence of postpartum anxiety in mothers.In addition,a greater maternal weight change,the presence of gestational diabetes mellitus,a shorter cervical length,a greater WBC count,and postpartum anxiety and depression were identified as risk factors for PTB.

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

Research on Information Architecture Design of Short-Form Video Social Platforms Based on Cognitive Psychology

This study investigates how cognitive psychology principles can be integrated into the information architecture design of short-form video platforms,like TikTok,to enhance user experience,engagement,and sharing.Using a questionnaire,it explores TikTok users’habits and preferences,highlighting how social media fatigue(SMF)impacts their interaction with the platform.The paper offers strategies to optimize TikTok’s design.It suggests refining the organizational system using principles like chunking,schema theory,and working memory capacity.Additionally,it proposes incorporating shopping features within TikTok’s interface to personalize product suggestions and enable monetization for influencers and content creators.Furthermore,the study underlines the need to consider gender differences and user preferences in improving TikTok’s sharing features,recommending streamlined and customizable sharing options,collaborative sharing,and a system to acknowledge sharing milestones.Aiming to strengthen social connections and increase sharing likelihood,this research provides insights into enhancing information architecture for short-form video platforms,contributing to their growth and success.

Study on the Influence of Food Packaging on the Psychology of Different Groups of Consumers

With the vigorous development of consumer culture in today’s society,various types of food packaging also appear in front of consumers in different forms.There are very big differences in food packaging in terms of shape,color,style and other aspects of information transmission,which have the most direct impact on the audience’s food consumption needs.Driven by the consumption-oriented society,food packaging has shown very obvious comprehensive characteristics,is significantly interdisciplinary,and has close connections with other disciplines.This article will analyze and sort out the impact of food packaging on consumer psychology from different perspectives.

Exploration of Child-friendly Urban Park Design Strategies Based on Child Behavioral Psychology

By studying the behavioral psychology and needs of children,this paper analyzes the essence of child-friendly parks from the perspective of children,and discusses the design principles and key points of child-friendly parks,so as to provide a practical basis for the construction of child-friendly cities.

Application of Color Psychology in Korean Brand Identity Design-Etude House

The purpose of this analysis is to delve into the application of color psychology in the logo design of Korean cosmetic brands,using Etude House as an example for an exhaustive analysis.By examining the history of the Etude House brand,the evolution of the logo design,and the changes in color choices,we analyze the traditional concepts of color symbolism in Korean culture and the culture of color in contemporary society in order to reveal the important role of color in cosmetic brand image.Through an in-depth analysis of the use of color in Etude House’s brand identity,we further analyze the impact of color on consumer emotions and purchasing behavior,as well as the potential impact of brand identity changes on market performance.Finally,the conclusions of the analysis summarize the practical application of color psychology in Etude House’s brand logo design,suggest recommendations for other Korean cosmetic brands to draw upon in their logo design,and discuss future directions.

Nonlinear Relationship and Its Evolutionary Trace between Node Degree and Average Path Length of China Aviation Network Based on Complex Network

In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the node degree and the average path length of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the node average path length of China aviation network in 1988,1994,2001,2008 and 2015 was calculated.Through regression analysis,it was found that the node degree had a logarithmic relationship with the average length of node path,and the two parameters of the logarithmic relationship had linear evolutionary trace.Key word:China aviation network,complex network,node degree,average length of node path,logarithmic relationship,evolutionary trace.

A Fast Clustering Based Evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable landscapes.However,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision variables.To tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal solutions.The proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search space.More importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by GPUs.While existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real variables.Furthermore,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.

Mating Strategies in the Context of Culture and Evolutionary Psychology

This paper comparatively studies the research outcome of the mating strategies by evolutionary psychologists and concludes that the mating preference is related to the two original natures of human beings—libido and appetite,and it is the mental mechanism of the coordination of the two.Preferences related to existence come from the appetite mechanism;while preferences related to reproduction come from the libido mechanism.The appetite mechanism and the libido mechanism are independent as well as unified,and they can meet the physiological need,the reproduction need of human beings and realize the survival value and psychological value,the essence of which is to pursue the happiness of human nature.

Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

The recent development of channel technology has promised to reduce the transaction verification time in blockchain operations.When transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each other.If one party refuses to do so,the channel is unstable.A stable channel is thus required.Because nodes may show uncooperative behavior,they may have a negative impact on the stability of such channels.In order to address this issue,this work proposes a dynamic evolutionary game model based on node behavior.This model considers various defense strategies'cost and attack success ratio under them.Nodes can dynamically adjust their strategies according to the behavior of attackers to achieve their effective defense.The equilibrium stability of the proposed model can be achieved.The proposed model can be applied to general channel networks.It is compared with two state-of-the-art blockchain channels:Lightning network and Spirit channels.The experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable state.Thus its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty

Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially.While existing methods could produce high-optimality results,they cannot be applied to real-time optimization for large-scale reservoirs due to high computational demands.In addition,most methods generally assume that the reservoir model is deterministic and ignore the uncertainty of the subsurface environment,making the obtained scheme unreliable for practical deployment.In this work,an efficient and robust method,namely evolutionaryassisted reinforcement learning(EARL),is proposed to achieve real-time production optimization under uncertainty.Specifically,the production optimization problem is modeled as a Markov decision process in which a reinforcement learning agent interacts with the reservoir simulator to train a control policy that maximizes the specified goals.To deal with the problems of brittle convergence properties and lack of efficient exploration strategies of reinforcement learning approaches,a population-based evolutionary algorithm is introduced to assist the training of agents,which provides diverse exploration experiences and promotes stability and robustness due to its inherent redundancy.Compared with prior methods that only optimize a solution for a particular scenario,the proposed approach trains a policy that can adapt to uncertain environments and make real-time decisions to cope with unknown changes.The trained policy,represented by a deep convolutional neural network,can adaptively adjust the well controls based on different reservoir states.Simulation results on two reservoir models show that the proposed approach not only outperforms the RL and EA methods in terms of optimization efficiency but also has strong robustness and real-time decision capacity.

Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data explosion.The generated data is collected and shared using edge-based IoT structures at a considerably high frequency.Thus,the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes.To address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme.In particular,we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes,where IoT devices and edge nodes are two parties of the game.IoT devices may make malicious requests to achieve their goals of stealing privacy.Accordingly,edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed.They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs.Built upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning strategy.Furthermore,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme.Therefore,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

Dose reconstruction with Compton camera during proton therapy via subset-driven origin ensemble and double evolutionary algorithm

Compton camera-based prompt gamma(PG) imaging has been proposed for range verification during proton therapy. However, a deviation between the PG and dose distributions, as well as the difference between the reconstructed PG and exact values, limit the effectiveness of the approach in accurate range monitoring during clinical applications. The aim of the study was to realize a PG-based dose reconstruction with a Compton camera, thereby further improving the prediction accuracy of in vivo range verification and providing a novel method for beam monitoring during proton therapy. In this paper, we present an approach based on a subset-driven origin ensemble with resolution recovery and a double evolutionary algorithm to reconstruct the dose depth profile(DDP) from the gamma events obtained by a cadmium-zinc-telluride Compton camera with limited position and energy resolution. Simulations of proton pencil beams with clinical particle rate irradiating phantoms made of different materials and the CT-based thoracic phantom were used to evaluate the feasibility of the proposed method. The results show that for the monoenergetic proton pencil beam irradiating homogeneous-material box phantom,the accuracy of the reconstructed DDP was within 0.3 mm for range prediction and within 5.2% for dose prediction. In particular, for 1.6-Gy irradiation in the therapy simulation of thoracic tumors, the range deviation of the reconstructed spreadout Bragg peak was within 0.8 mm, and the relative dose deviation in the peak area was less than 7% compared to the exact values. The results demonstrate the potential and feasibility of the proposed method in future Compton-based accurate dose reconstruction and range verification during proton therapy.