Abstract--This paper introduces a novel framework for enhancing Random Forest classifiers by integrating probabilistic feature sampling and hyperparameter tuning via Simulated Annealing. The proposed framework exhibits substantial advancements in predictive accuracy and generalization, adeptly tackling the multifaceted challenges of robust classification across diverse domains, including credit risk evaluation, anomaly detection in IoT ecosystems, early-stage medical diagnostics, and high-dimensional biological data analysis. T o overcome the limitations of conventional Random Forests, we present an approach that places stronger emphasis on capturing the most relevant signals from data while enabling adaptive hyperparameter configuration. The model is guided towards features that contribute more meaningfully to classification and optimizing this with dynamic parameter tuning. The results demonstrate consistent accuracy improvements and meaningful insights into feature relevance, showcasing the efficacy of combining importance aware sampling and metaheuristic optimization. RFs are widely used ensemble learning methods known for their robustness, interpretability, scalability and performance across diverse machine learning tasks.

We introduce SA-FDR, a novel algorithm for $\ell_0$-norm feature selection that considers this task as a combinatorial optimisation problem and solves it by using simulated annealing to perform a global search over the space of feature subsets. The optimisation is guided by the Fisher discriminant ratio, which we use as a computationally efficient proxy for model quality in classification tasks. Our experiments, conducted on datasets with up to hundreds of thousands of samples and hundreds of features, demonstrate that SA-FDR consistently selects more compact feature subsets while achieving a high predictive accuracy. This ability to recover informative yet minimal sets of features stems from its capacity to capture inter-feature dependencies often missed by greedy optimisation approaches. As a result, SA-FDR provides a flexible and effective solution for designing interpretable models in high-dimensional settings, particularly when model sparsity, interpretability, and performance are crucial.

This paper introduces the application of the Brain Drain Optimization algorithm -- a swarm-based metaheuristic inspired by the emigration of intellectual elites -- to the N-Queens problem. The N-Queens problem, a classic combinatorial optimization problem, serves as a challenge for applying the BRADO. A designed cost function guides the search, and the configurations are tuned using a TOPSIS-based multicriteria decision making process. BRADO consistently outperforms alternatives in terms of solution quality, achieving fewer threats and better objective function values. To assess BRADO's efficacy, it is benchmarked against several established metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), Iterated Local Search (ILS), and basic Local Search (LS). The study highlights BRADO's potential as a general-purpose solver for combinatorial problems, opening pathways for future applications in other domains of artificial intelligence.

This paper presents a novel method for solving the 2D advection-diffusion equation using fixed-depth symbolic regression and symbolic differentiation without expression trees. The method is applied to two cases with distinct initial and boundary conditions, demonstrating its accuracy and ability to find approximate solutions efficiently. This framework offers a promising, scalable solution for finding approximate solutions to differential equations, with the potential for future improvements in computational performance and applicability to more complex systems involving vector-valued objectives.

Modelling brain connectomes networks: Solv is a worthy competitor to hyperbolic geometry! Dorota Celi nska-Kopczy nska, Eryk Kopczy nski Institute of Informatics, University of Warsaw, Warsaw, Poland July 24, 2024 Abstract Finding suitable embeddings for connectomes (spatially embedded complex networks that map neural connections in the brain) is crucial for analyzing and understanding cognitive processes. Recent studies have found two-dimensional hyperbolic embeddings superior to Euclidean embeddings in modeling connectomes across species, especially human connectomes. However, those studies had limitations: geometries other than Euclidean, hyperbolic, or spherical were not considered. Following William Thurston's suggestion that the networks of neurons in the brain could be successfully represented in Solv geometry, we study the goodness-of-fit of the embeddings for 21 con-nectome networks (8 species). To this end, we suggest an embedding algorithm based on Simulating Annealing that allows us to embed con-nectomes to Euclidean, Spherical, Hyperbolic, Solv, Nil, and product geometries. Our algorithm tends to find better embeddings than the state-of-the-art, even in the hyperbolic case. Our findings suggest that while three-dimensional hyperbolic embeddings yield the best results in many cases, Solv embeddings perform reasonably well. 1 Introduction Connectomes are comprehensive maps of the neural connections in the brain. Understanding the interactions they shape is a key to understanding cognitive processes. Given their spatially embedded complexity, shaped by physical 1 arXiv:2407.16077v1 Therefore, a vast amount of recent research has been devoted to finding the appropriate embeddings for con-nectome networks. Recent studies (e.g., [WHKL22, AS20]) have advocated for the superiority of two-dimensional hyperbolic embeddings over Euclidean embeddings in modeling connectomes across species, especially human con-nectomes. However, those studies had limitations: they restricted the focus to Euclidean, hyperbolic, or spherical geometries, neglecting to explore other potential embedding spaces.

Using Quantum Computers to solve problems in Recommender Systems that classical computers cannot address is a worthwhile research topic. In this paper, we use Quantum Annealers to address the feature selection problem in recommendation algorithms. This feature selection problem is a Quadratic Unconstrained Binary Optimization (QUBO) problem. By incorporating Counterfactual Analysis, we significantly improve the performance of the item-based KNN recommendation algorithm compared to using pure Mutual Information. Extensive experiments have demonstrated that the use of Counterfactual Analysis holds great promise for addressing such problems.

Unquestionably, in recent decades, Deep Neural Networks (DNNs) have been by far the most widely used tools to perform complex machine learning tasks. More recently, DNNs have been used in cyber-physical systems critical to public security and integrity; such as autonomous vehicle driving and air traffic systems. Therefore, it is of pressing interest to implement security verification systems for DNNs. One of the objectives in this line of interest is the verification of the maximum and minimum values assumed by a DNN, an objective commonly known as the range estimation problem, see Dutta et al. [2018], Wang et al. [2018]. However, the relationships established between the inputs and outputs of a DNN are highly non-linear and complex, difficult to understand with existing tools today. Due to this inability, DNNs are commonly referred to as black boxes. This nature of DNN makes the range estimation problem particularly challenging, because there is no geometric information about the response surface generated by a DNN. For example, if local geometric information about the generated surface were obtained, such as the gradient vector and the Hessian matrix at each point, the problem could be addressed with conventional nonlinear programming techniques. However, in a DNN it is only possible to obtain point information about the estimated response, without any local knowledge around that point.

The efficient scheduling of permanent and temporary workers is crucial for Improving the efficiency of sortation center management optimizing the efficiency of the logistics depot while has a direct impact on the fulfillment efficiency and minimizing labor usage. The study begins by establishing operational costs of the entire logistics network. Staff a 0-1 integer linear programming model, with decision management in sortation centers is a key challenge. Staffing needs to be adjusted according to the forecasted shipment variables determining the scheduling of permanent and volume to ensure a sufficient workforce to handle the flow of temporary workers for each time slot on a given day. The goods during peak hours while avoiding the wastage of excess objective function aims to minimize person-days, while manpower during low-demand times. Staff scheduling based constraints ensure fulfillment of hourly labor on effective solution algorithms becomes one of the key requirements, limit workers to one time slot per day, cap strategies to improve the efficiency of the sorting center. By consecutive working days for permanent workers, and reasonably allocating regular and temporary workers, the maintain non-negativity and integer constraints. The sorting speed and accuracy can be improved, thus reducing the model is then solved using genetic algorithms and overall logistics cost and improving customer satisfaction.

Reinforcement learning (RL) for physical design of silicon chips in a Google 2021 Nature paper stirred controversy due to poorly documented claims that raised eyebrows and drew critical media coverage. The paper withheld critical methodology steps and most inputs needed to reproduce results. Our meta-analysis shows how two separate evaluations filled in the gaps and demonstrated that Google RL lags behind (i) human designers, (ii) a well-known algorithm (Simulated Annealing), and (iii) generally-available commercial software, while being slower; and in a 2023 open research contest, RL methods weren't in top 5. Crosschecked data indicate that the integrity of the Nature paper is substantially undermined owing to errors in conduct, analysis and reporting. Before publishing, Google rebuffed internal allegations of fraud. We note policy implications and conclusions for chip design.

To meet the growing need for computational power for DNNs, multiple specialized hardware architectures have been proposed. Each DNN layer should be mapped onto the hardware with the most efficient schedule, however, SotA schedulers struggle to consistently provide optimum schedules in a reasonable time across all DNN-HW combinations. This paper proposes SALSA, a fast dual-engine scheduler to generate optimal execution schedules for both even and uneven mapping. We introduce a new strategy, combining exhaustive search with simulated annealing to address the dynamic nature of the loop ordering design space size across layers. SALSA is extensively benchmarked against two SotA schedulers, LOMA and Timeloop on 5 different DNNs, on average SALSA finds schedules with 11.9% and 7.6% lower energy while speeding up the search by 1.7x and 24x compared to LOMA and Timeloop, respectively.