Exploration-exploitation Trade-off Research Articles

Uncovering multiscale structure in the variability of larval zebrafish navigation

Animals chain movements into long-lived motor strategies, exhibiting variability across scales that reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build Markov models of movement sequences that bridge across timescales and enable a quantitative comparison of behavioral phenotypes among individuals. Applied to larval zebrafish responding to diverse sensory cues, we uncover a hierarchy of long-lived motor strategies, dominated by changes in orientation distinguishing cruising versus wandering strategies. Environmental cues induce preferences along these modes at the population level: while fish cruise in the light, they wander in response to aversive stimuli, or in search for appetitive prey. As our method encodes the behavioral dynamics of each individual fish in the transitions among coarse-grained motor strategies, we use it to uncover a hierarchical structure in the phenotypic variability that reflects exploration-exploitation trade-offs. Across a wide range of sensory cues, a major source of variation among fish is driven by prior and/or immediate exposure to prey that induces exploitation phenotypes. A large degree of variability that is not explained by environmental cues unravels hidden states that override the sensory context to induce contrasting exploration-exploitation phenotypes. Altogether, by extracting the timescales of motor strategies deployed during navigation, our approach exposes structure among individuals and reveals internal states tuned by prior experience.

Curiosity-Driven Multi-Level Intrinsic Reward DQN for Enhanced Exploration in Reinforcement Learning

Abstract. Reinforcement learning (RL) has shown great potential in solving complex decision-making tasks. However, efficient exploration remains a significant challenge, particularly in environments with sparse or deceptive rewards. This paper introduces DQN-Mult-Cur, a novel reinforcement learning algorithm that enhances exploration by integrating curiosity-driven and multi-level intrinsic rewards within the Deep Q-Network (DQN) framework. The proposed method addresses the limitations of conventional exploration strategies by incentivizing agents to explore novel and meaningful states, thereby improving learning efficiency and performance. Extensive experiments across three standard environmentsCartPole-v1, MountainCar-v0 and Acrobot-v1demonstrate that DQN-Mult-Cur outperforms traditional DQN variants, achieving faster convergence, higher rewards, and greater stability. An ablation study further highlights the importance of each intrinsic reward component, confirming the robustness of the proposed approach. The results suggest that DQN-Mult-Cur offers a comprehensive solution to the exploration-exploitation trade-off in reinforcement learning, making it applicable to a wide range of challenging environments.

Performance Comparison of UCB, TS, and -Greedy TS Algorithms through Simulation of Multi-Armed Bandit Machine

Multi-Armed Bandit (MAB) algorithms are classic algorithms that address sequential decision-making under uncertainty by solving the exploration-exploitation trade-off dilemma. This study investigates the performance comparison of multiple MAB algorithms using a simulated multi-armed bandit machine with Bernoulli reward distribution as the experimental environment. This study compares the performance differences among Upper Confidence Bound (UCB), Thompson Sampling (TS) and -Greedy Thompson Sampling (-TS) in this environment, and attempts to set different parameters, namely the number of arms and the number of experimental rounds and calculate the corresponding cumulative regret of the algorithm under various conditions. The size of the cumulative regret reflects the performance of each algorithm in the simulated slot machine model with rewards that conform to the Bernoulli distribution. In addition, the algorithm running time under the same conditions is also recorded to analyze from the perspective of algorithm efficiency. The experimental results show that under the experimental environment of this study, the cumulative regret produced by the UCB algorithm is more than three times that of the other two algorithms. When the number of trials is small, the cumulative regret generated by the TS algorithm is small, but overall, the performance of the TS algorithm and the -TS algorithm set in this experiment in minimizing the cumulative regret is not much different. However, TS runs in a shorter time under the same conditions. The results of this experiment show that after the number of rounds of experimental operation reaches a large enough number, the operating efficiency of the TS algorithm will be significantly higher than that of the -TS algorithm. TS algorithms have higher randomness, so they show better performance under this experimental condition. The -TS under the parameter setting of this study encourages exploration more in the early stage of the experiment, so it will produce greater cumulative regret than the traditional TS algorithm. In the long run, the performance difference between the two in the multi-armed bandit problem with Bernoulli distribution of rewards is very small. However, the TS algorithm has more advantages in algorithm execution efficiency, so when solving similar problems, the TS algorithm is a better choice.

Mixed Thermal and Renewable Energy Generation Optimization in Non-Interconnected Regions via Boolean Mapping

Global efforts aiming to shift towards renewable energy and smart grid configurations require accurate unit commitment schedules to guarantee power balance and ensure feasible operation under different complex constraints. Intelligent systems utilizing hybrid and high-level techniques have arisen as promising solutions to provide optimum exploration–exploitation trade-offs at the expense of computational complexity. To ameliorate this requirement, which is extremely expensive in non-interconnected renewable systems, radically different approaches based on enhanced priority schemes and Boolean encoding/decoding have to take place. This compilation encompasses various mappings that convert multi-valued clausal forms into Boolean expressions with equivalent satisfiability. Avoiding any need to introduce prior parameter settings, the solution utilizes state-of-the-art advancements in the field of artificial intelligence models, namely Boolean mapping. It allows for the efficient identification of the optimal configuration of a non-convex system with binary and discontinuous dynamics in the fewest possible trials, providing impressive performance. In this way, Boolean mapping becomes capable of providing global optimum solutions to unit commitment utilizing fully tractable procedures without deteriorating the computational time. The results, considering a non-interconnected power system, show that the proposed model based on artificial intelligence presents advantageous performance in terms of generating cost and complexity. This is particularly important in isolated networks, where even a-not-so great deviation between production and consumption may reflect as a major disturbance in terms of frequency and voltage.

A causal role of the right dorsolateral prefrontal cortex in random exploration

Decision to explore new options with uncertain outcomes or exploit familiar options with known outcomes is a fundamental challenge that the brain faces in almost all real-life decisions. Previous studies have shown that humans use two main explorative strategies to negotiate this explore-exploit tradeoff. Exploring for the sake of information is called directed exploration, and exploration driven by behavioral variability is known as random exploration. While previous neuroimaging studies have shown different neural correlates for these explorative strategies, including right frontopolar cortex (FPC), right dorsolateral prefrontal cortex (DLPFC), and dorsal anterior cingulate cortex (dACC), there is still a lack of causal evidence for most of these brain regions. Here, we focused on the right DLPFC, which was previously supported to be involved in exploration. Using the continuous theta burst stimulation (cTBS) and Horizon task on twenty-five healthy right-handed adult participants, we showed that inhibiting rDLPFC did not change directed exploration but selectively reduced random exploration, by increasing reward sensitivity over the average reward of each option. This suggests a causal role for rDLPFC in random exploration, and further supports dissociable neural implementations for these two explorative strategies.

Formal contracts mitigate social dilemmas in multi-agent reinforcement learning

Multi-agent Reinforcement Learning (MARL) is a powerful tool for training autonomous agents acting independently in a common environment. However, it can lead to sub-optimal behavior when individual incentives and group incentives diverge. Humans are remarkably capable at solving these social dilemmas. It is an open problem in MARL to replicate such cooperative behaviors in selfish agents. In this work, we draw upon the idea of formal contracting from economics to overcome diverging incentives between agents in MARL. We propose an augmentation to a Markov game where agents voluntarily agree to binding transfers of reward, under pre-specified conditions. Our contributions are theoretical and empirical. First, we show that this augmentation makes all subgame-perfect equilibria of all Fully Observable Markov Games exhibit socially optimal behavior, given a sufficiently rich space of contracts. Next, we show that for general contract spaces, and even under partial observability, richer contract spaces lead to higher welfare. Hence, contract space design solves an exploration-exploitation tradeoff, sidestepping incentive issues. We complement our theoretical analysis with experiments. Issues of exploration in the contracting augmentation are mitigated using a training methodology inspired by multi-objective reinforcement learning: Multi-Objective Contract Augmentation Learning. We test our methodology in static, single-move games, as well as dynamic domains that simulate traffic, pollution management, and common pool resource management.

Social exploration: How and why people seek new connections.

Just as animals forage for food, humans forage for social connections. People often face a decision between exploring new relationships versus deepening existing ones. This trade-off, known in optimal foraging theory as the exploration-exploitation trade-off, is featured prominently in other disciplines such as animal foraging, learning, and organizational behavior. Many of the framework's principles can be applied to humans' choices about their social resources, which we call social exploration/exploitation. Using known principles in the domain of social exploration/exploitation can help social psychologists better understand how and why people choose their relationships, which ultimately affect their health and well-being. In this article, we discuss the costs and benefits of social exploration and social exploitation. We then synthesize known person- and situation-level predictors of social decision making, reframing them in the language of the explore-exploit trade-off. We propose that people explore more when they find it more rewarding and less costly, and when the environment has many opportunities to do so. We conclude by discussing hypotheses generated by applying optimal foraging theory to social decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Design of an Integrated Method for Optimizing Electron Beam Lithography Using Genetic Algorithms, Particle Swarm Optimization, and Grey Wolf Optimizer

With the ever-increasing demands of advanced manufacturing for precise micro-and nanoscale patterning, optimization of the EBL process is in urgent need. The current optimization methods involve combinations such as GA with GWO or PSO with GWO, which are plagued by poor exploration-exploitation trade-offs and hence converge to suboptimal solutions or insufficient refinement of the solution. The above challenges are overcome by an innovative Adaptive Wolf-Driven Swarm Evolution approach, synergizing the strengths of GA, PSO, and GWO for the optimization process of EBL. The generation of a diversified population of solutions from GA is the starting of AWDSE ensuring wide explorations in the search space. Further, a role-based classification using GWO classifies solutions hierarchically into different roles: Alpha, Beta, Gamma, Delta. Solutions at higher ranks (Alpha, Beta) are then refined by PSO-based updates that exploit the search space by updating the solutions, while the solutions ranked lower (Gamma, Delta) are subjected to GA-driven crossover and mutation operations to maintain diversity and exploration. The adaptive switching between the evolutionary operations of GA and particle updates of PSO may definitely be driven by the leadership dynamics of GWO, which allows more intensive balancing of diversification-intensification, thus allowing improved convergence precision and velocity. Experimental results prove that AWDSE is able to obtain an improvement of about 18% reduction of critical dimensions with a shrinkage in delay time amounting to 12%, outperforming traditional approaches like GA-GWO and PSO-GWO. This advance underlines the possibility of AWDSE to significantly enhance EBL efficiency and accuracy, with vistas toward ever finer and faster ways of nanomanufacturing process.

Dopamine and Norepinephrine Differentially Mediate the Exploration-Exploitation Tradeoff.

Dopamine (DA) and norepinephrine (NE) have been repeatedly implicated in neuropsychiatric vulnerability, in part via their roles in mediating the decision-making processes. Although two neuromodulators share a synthesis pathway and are coactivated under states of arousal, they engage in distinct circuits and modulatory roles. However, the specific role of each neuromodulator in decision-making, in particular the exploration-exploitation tradeoff, remains unclear. Revealing how each neuromodulator contributes to exploration-exploitation tradeoff is important in guiding mechanistic hypotheses emerging from computational psychiatric approaches. To understand the differences and overlaps of the roles of these two catecholamine systems in regulating exploration, a direct comparison using the same dynamic decision-making task is needed. Here, we ran male and female mice in a restless two-armed bandit task, which encourages both exploration and exploitation. We systemically administered a nonselective DA antagonist (flupenthixol), a nonselective DA agonist (apomorphine), a NE beta-receptor antagonist (propranolol), and a NE beta-receptor agonist (isoproterenol) and examined changes in exploration within subjects across sessions. We found a bidirectional modulatory effect of dopamine on exploration. Increasing dopamine activity decreased exploration and decreasing dopamine activity increased exploration. The modulatory effect of beta-noradrenergic receptor activity on exploration was mediated by sex. Reinforcement learning model parameters suggested that dopamine modulation affected exploration via decision noise and norepinephrine modulation affected exploration via sensitivity to outcome. Together, these findings suggested that the mechanisms that govern the exploration-exploitation transition are sensitive to changes in both catecholamine functions and revealed differential roles for NE and DA in mediating exploration.

Information-directed policy sampling for episodic Bayesian Markov decision processes

We consider finite-stage Markov Decision Processes (MDPs) under incomplete information, where the decision-maker only knows that the true transition probability and reward matrices belong to given, finite sets. The decision-maker interacts with the system over a finite number of episodes. The first episode begins with a probabilistic belief about the true probability and reward matrices. This belief is updated at the end of each episode using observed events. The goal is to maximize the expected total reward earned over all episodes. In the resulting model-based episodic Bayesian MDP, it suffices to only consider (the known) policies that are optimal to each one of the possible probability and reward matrices. Nevertheless, the decision-maker should execute policies that provide information about the true probabilities and rewards (exploration), but also exploit this knowledge to increase rewards. We propose a framework called Information-Directed Policy Sampling (IDPS). In each episode, the decision-maker balances the exploitation-exploration trade-off by executing a randomized policy that minimizes a so-called convex information ratio. We derive a regret bound that is independent of state- and action-space cardinalities when the set of matrices is exogenously determined. Numerical experiments show IDPS outperforming a state-of-the-art approach called Posterior Sampling.

Mating with Multi-Armed Bandits: Reinforcement Learning Models of Human Mate Search.

Mate choice requires navigating an exploration-exploitation trade-off. Successful mate choice requires choosing partners who have preferred qualities; but time spent determining one partner's qualities could have been spent exploring for potentially superior alternatives. Here I argue that this dilemma can be modeled in a reinforcement learning framework as a multi-armed bandit problem. Moreover, using agent-based models and a sample of k = 522 real-world romantic dyads, I show that a reciprocity-weighted Thompson sampling algorithm performs well both in guiding mate search in noisy search environments and in reproducing the mate choices of real-world participants. These results provide a formal model of the understudied psychology of human mate search. They additionally offer implications for our understanding of person perception and mate choice.

Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks

Bayesian statistics are popular in human cognitive neuroscience research because they can incorporate prior knowledge. Although well established for retrospective analysis, the application of Bayesian methods to prospective analysis is less well developed, especially when used in combination with computational model-based analysis of behavioural data. It is therefore important to establish effective methods for testing and optimising experimental designs for these purposes. One potential framework for a prospective approach is Bayes factor design analysis (BFDA), which can be used alongside latent variable modelling to evaluate and visualise the distribution of Bayes factors for a given experimental design. This paper provides a tutorial-style analysis combining BFDA with latent variable modelling to evaluate exploration-exploitation trade-offs in the binary multi-armed bandit task (MAB). This is a particularly tricky example of human decision-making with which to investigate the feasibility of differentiating latent variables between groups as a function of different design parameters. We examined how sample size, number of games per participant and effect size affect the strength of evidence supporting a difference in means between two groups. To further assess how these parameters affect experimental results, metrics of error were evaluated. Using simulations, we demonstrated how BFDA can be combined with latent variable modelling to evaluate and optimise parameter estimation of exploration in the MAB task, allowing effective inference of the mean degree of random exploration in a population, as well as between groups. However, BFDA indicated that, even with large samples and effect sizes, there may be some circumstances where there is a high likelihood of errors and a low probability of detecting evidence in favour of a difference when comparing random exploration between two groups performing the bandit task. In summary, we show how BFDA can prospectively inform design and power of human behavioural tasks.

Advancing decision-making strategies through a comprehensive study of Multi-Armed Bandit algorithms and applications

Multi-Armed Bandit (MAB) strategies play a pivotal role in decision-making algorithms by adeptly managing the exploration-exploitation trade-off in environments characterized by multiple options and constrained resources. This paper delves into the core MAB algorithms, including Explore-Then-Commit (ETC), Thompson Sampling, and Upper Confidence Bound (UCB). It provides a detailed examination of their theoretical underpinnings and their application across diverse sectors such as recommender systems, healthcare, and finance. MAB algorithms are celebrated for their efficiency in optimizing decision outcomes; however, they are not without challenges. Significant issues include managing the complexity of exploration and adapting to non-stationary environments where the dynamics of the available options may change over time. A nuanced understanding of these challenges is crucial for effectively implementing MAB strategies in complex decision-making scenarios. This study not only highlights the versatility and potential of MAB algorithms but also underscores the need for ongoing research to refine these techniques and expand their applicability.

An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach

This paper addresses the enhancement of multiple Unmanned Aerial Vehicle (UAV) swarm formation control in challenging terrains through the novel fractional memetic computing approach known as fractional-order velocity-pausing particle swarm optimization (FO-VPPSO). Existing particle swarm optimization (PSO) algorithms often suffer from premature convergence and an imbalanced exploration–exploitation trade-off, which limits their effectiveness in complex optimization problems such as UAV swarm control in rugged terrains. To overcome these limitations, FO-VPPSO introduces an adaptive fractional order β and a velocity pausing mechanism, which collectively enhance the algorithm’s adaptability and robustness. This study leverages the advantages of a meta-heuristic computing approach; specifically, fractional-order velocity-pausing particle swarm optimization is utilized to optimize the flying path length, mitigate the mountain terrain costs, and prevent collisions within the UAV swarm. Leveraging fractional-order dynamics, the proposed hybrid algorithm exhibits accelerated convergence rates and improved solution optimality compared to traditional PSO methods. The methodology involves integrating terrain considerations and diverse UAV control parameters. Simulations under varying conditions, including complex terrains and dynamic threats, substantiate the effectiveness of the approach, resulting in superior fitness functions for multi-UAV swarms. To validate the performance and efficiency of the proposed optimizer, it was also applied to 13 benchmark functions, including uni- and multimodal functions in terms of the mean average fitness value over 100 independent trials, and furthermore, an improvement at percentages of 29.05% and 2.26% is also obtained against PSO and VPPSO in the case of the minimum flight length, as well as 16.46% and 1.60% in mountain terrain costs and 55.88% and 31.63% in collision avoidance. This study contributes valuable insights to the optimization challenges in UAV swarm-formation control, particularly in demanding terrains. The FO-VPPSO algorithm showcases potential advancements in swarm intelligence for real-world applications.

Improved synergistic swarm optimization algorithm to optimize task scheduling problems in cloud computing

Cloud computing has emerged as a cornerstone technology for modern computational paradigms due to its scalability and flexibility. One critical aspect of cloud computing is efficient task scheduling, which directly impacts system performance and resource utilization. In this paper, we propose an enhanced optimization algorithm tailored for task scheduling in cloud environments. Building upon the foundation of the Jaya algorithm and Synergistic Swarm Optimization (SSO), our approach integrates a Levy flight mechanism to enhance exploration-exploitation trade-offs and improve convergence speed. The Jaya algorithm's ability to exploit the current best solutions is complemented by the SSO's collaborative search strategy, resulting in a synergistic optimization framework. Moreover, the incorporation of Levy flights injects stochasticity into the search process, enabling the algorithm to escape local optima and navigate complex solution spaces more effectively. We evaluate the proposed algorithm against state-of-the-art approaches using benchmark task scheduling problems in cloud environments. Experimental results demonstrate the superiority of our method in terms of solution quality, convergence speed, and scalability. Overall, our proposed Improved Jaya Synergistic Swarm Optimization Algorithm offers a promising solution for optimizing TSCC (TSCC), contributing to enhanced resource utilization and system performance in cloud-based applications. The proposed method got 88 % accuracy overall and 10 % enhancement compared to the original method.

Comparing Human Behavior to an Optimal Policy for Innovation

Human learning does not stop at solving a single problem. Instead, we seek new challenges, define new goals, and come up with new ideas. Unlike the classic explore-exploit trade-off between known and unknown options, making new tools or generating new ideas is not about collecting data from existing unknown options, but rather about create new options out of what is currently available. We introduce a discovery game designed to study how rational agents make decisions about pursuing innovations, where discovering new ideas is a process of combining existing ideas in an open-ended compositional space. We derive optimal policies of this decision problem formalized as a Markov decision process, and compare people's behaviors to the model predictions in an online behavioral experiment. We found evidence that people both innovate rationally, guided by potential returns in this discovery game, and under- and over-explore systematically in different settings.

Optimizing Movie Recommendation Systems with Multi-Armed Bandit Algorithms

In the current digital era, with the proliferation of online content, users are often overwhelmed by an abundance of digital advertisements, a consequence of the lower costs associated with digital channels compared to traditional media. Amidst this information deluge, recommendation systems have become pivotal in offering tailored suggestions to users. The Multi-Armed Bandit (MAB) problem, rooted in reinforcement learning, presents an effective approach for addressing the exploration-exploitation trade-offs inherent in recommendation systems. This paper specifically examines the implementation of MAB algorithms within the context of movie recommendation systems. A thorough literature review is conducted, focusing on the synergy between reinforcement learning and MAB algorithms, their role in recommendation systems, and an overview of fundamental MAB algorithms. To assess the efficacy of these algorithms, an experimental approach is designed, leveraging real-world datasets. This approach encompasses the formulation of the problem, data collection methods, selection of hyperparameters, and analysis of empirical results. The findings from these experiments indicate that Thompson Sampling stands out as the most effective MAB algorithm in the context of the studied datasets. The results of this study suggest that MAB algorithms have considerable potential to enhance the effectiveness of movie recommendation systems. Furthermore, the methodology and framework proposed in this paper offer valuable insights for integrating and combining multiple algorithms in complex decision-making scenarios.

Advancements in Reinforcement Learning

Reinforcement Learning (RL) stands at the forefront of machine learning, offering a paradigm where agents learn to navigate complex environments through trial and error interactions. This paper provides a comprehensive overview of RL concepts, algorithms, challenges, and future directions. Key concepts such as agent-environment interaction, Markov Decision Processes (MDPs), value-based and policy-based methods are elucidated. Challenges including the exploration-exploitation trade-off, sample efficiency, and safety concerns are discussed, alongside potential breakthroughs in deep RL, handling continuous action spaces, and dealing with partial observability. The integration of RL with emerging technologies like IoT and its implications for real-world applications such as robotics, autonomous vehicles, and healthcare are explored. Finally, future trends and directions in RL research, including AI engineering and automated feature engineering, are outlined, highlighting the potential for transformative advancements and their impact on various domains. This paper serves as a comprehensive resource for researchers, practitioners, and enthusiasts interested in the evolving landscape of Reinforcement Learning

Socio-economic constraints to low-carbon transitions: insights from Kazakhstan’s Emissions Trading Scheme

ABSTRACT In this research, we examine energy facilities’ (both electricity and heat) responses to emissions trading in Kazakhstan, a fossil-fuel-dependent developing country, to understand why facilities have delayed the adoption of substantial carbon mitigation measures. Our research relies on the frameworks of sociotechnical systems (STS) and firm-level exploration-exploitation trade-offs (EET) because energy transitions are dependent on firm-level characteristics as well as industry – and society-level environments. We find poorly designed and inconsistent regulations, ambivalence to the carbon emissions, challenges from vested interests, energy security regulations and desire for stable socio-economic conditions to be the main society-level factors impeding the effectiveness of the Kazakhstan Emissions Trading Scheme (KazETS). The main industry-level factors hindering the KazETS were found to be the involvement of the state-owned enterprise (SOE) Samruk Energo which accesses government funding for coal-based facility investment, abundant coal reserves that are extracted at low cost, increasing costs and decreasing quality of fuel, relatively very high cost of natural gas, increasing demand for electricity and heat, lack of market competition and no international firms operating in energy generation. Finally, the main firm-level factors restricting investment in low-carbon technologies are low market incentives for investment to replace technologically obsolete assets, the lack of firm-level carbon strategies and very low capacity of workers.

Adaptive feature selection for active trachoma image classification

Trachoma is a neglected tropical eye disease caused by ocular strains of Chlamydia trachomatis, which affects millions of people worldwide. To examine the eye for signs of active trachoma, healthcare providers typically look for clusters of five or more follicles on the conjunctiva of the upper eyelid for the follicular inflammatory trachoma stage. However, it is also possible to find individual follicles scattered throughout the conjunctiva, particularly in mild or early-stage trachoma cases. Additionally, the datasets are photographic images collected in the field that can be high-dimensional and may contain large amounts of redundant information. We propose integrating novel attention-based feature extraction and feature selection techniques to address these challenges. First, we present the Lambda layer within the Convolutional Block Attention Module (L-CBAM) to normalize attention weights and improve the feature extraction process. Second, we introduce an adaptive mechanism, Adaptive Beta Hill Climbing (AβHC) with Social Ski-Driver (SSD), which adjusts the exploration-exploitation trade-off during the search process, allowing for better exploration of the search space and more efficient convergence toward an optimal feature subset. We then use the multilayer perceptron (MLP) classifier to produce final classification results using selected subsets. We evaluated the proposed approach on active trachoma inverted eyelid images and obtained accuracy scores of 93.3% with only 19.7% of the selected features, surpassing many of the algorithms used for comparison. Our proposed method has demonstrated excellent performance compared to recent works utilizing the same datasets. The source code of this work is available at https://github.com/mshitie2/Active_Trachoma.