Learning Search Algorithms Research Articles

Learning search algorithm: framework and comprehensive performance for solving optimization problems

In this study, the Learning Search Algorithm (LSA) is introduced as an innovative optimization algorithm that draws inspiration from swarm intelligence principles and mimics the social learning behavior observed in humans. The LSA algorithm optimizes the search process by integrating historical experience and real-time social information, enabling it to effectively navigate complex problem spaces. By doing so, it enhances its global development capability and provides efficient solutions to challenging optimization tasks. Additionally, the algorithm improves the collective learning capacity by incorporating teaching and active learning behaviors within the population, leading to improved local development capabilities. Furthermore, a dynamic adaptive control factor is utilized to regulate the algorithm’s global exploration and local development abilities. The proposed algorithm is rigorously evaluated using 40 benchmark test functions from IEEE CEC 2014 and CEC 2020, and compared against nine established evolutionary algorithms as well as 11 recently improved algorithms. The experimental results demonstrate the superiority of the LSA algorithm, as it achieves the top rank in the Friedman rank-sum test, highlighting its power and competitiveness. Moreover, the LSA algorithm is successfully applied to solve six real-world engineering problems and 15 UCI datasets of feature selection problems, showcasing its significant advantages and potential for practical applications in engineering problems and feature selection problems.

AutoDDI: Drug-Drug Interaction Prediction With Automated Graph Neural Network.

Drug-drug interaction (DDI) has attracted widespread attention because when incompatible drugs are taken together, DDI will lead to adverse effects on the body, such as drug poisoning or reduced drug efficacy. The adverse effects of DDI are closely determined by the molecular structures of the drugs involved. To represent drug data effectively, researchers usually treat the molecular structure of drugs as a molecule graph. Then, previous studies can use the handcrafted graph neural network (GNN) model to learn the molecular graph representations of drugs for DDI prediction. However, in the field of bioinformatics, manually designing GNN architectures for specific molecular structure datasets is time-consuming and depends on expert experience. To address this problem, we propose an automatic drug-drug interaction prediction method named AutoDDI that can efficiently and automatically design the GNN architecture for drug-drug interaction prediction without manual intervention. To this end, we first design an effective search space for drug-drug interaction prediction by revisiting various handcrafted GNN architectures. Then, to efficiently and automatically design the optimal GNN architecture for each drug dataset from the search space, a reinforcement learning search algorithm is adopted. The experiment results show that AutoDDI can achieve the best performance on two real-world datasets. Moreover, the visual interpretation results of the case study show that AutoDDI can effectively capture drug substructure for drug-drug interaction prediction.

Learning search algorithm to solve real-world optimization problems and parameter extract of photovoltaic models

Learning search algorithm to solve real-world optimization problems and parameter extract of photovoltaic models

Exploring parameter spaces with artificial intelligence and machine learning black-box optimization algorithms

Constraining beyond the Standard Model theories usually involves scanning highly multidimensional parameter spaces and checking observable predictions against experimental bounds and theoretical constraints. Such a task is often timely and computationally expensive, especially when the model is severely constrained and thus leading to very low random sampling efficiency. In this work we tackled this challenge using artificial intelligence and machine learning search algorithms used for black-box optimization problems. Using the constrained minimal supersymmetric standard model and the phenomenological minimal supersymmetric standard model parameter spaces, we consider both the Higgs mass and the dark matter relic density constraints to study their sampling efficiency and parameter space coverage. We find our methodology to produce orders of magnitude improvement of sampling efficiency while reasonably covering the parameter space.

First machine learning gravitational-wave search mock data challenge

We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge. For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 s with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and run-time for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs $\ensuremath{\ge}200$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.

A learning search algorithm with propagational reinforcement learning

When reinforcement learning with a deep neural network is applied to heuristic search, the search becomes a learning search. In a learning search system, there are two key components: (1) a deep neural network with sufficient expression ability as a heuristic function approximator that estimates the distance from any state to a goal; (2) a strategy to guide the interaction of an agent with its environment to obtain more efficient simulated experience to update the Q-value or V-value function of reinforcement learning. To date, neither component has been sufficiently discussed. This study theoretically discusses the size of a deep neural network for approximating a product function of p piecewise multivariate linear functions. The existence of such a deep neural network with O(n + p) layers and O(dn + dnp + dp) neurons has been proven, where d is the number of variables of the multivariate function being approximated, 𝜖 is the approximation error, and n = O(p + log2(pd/𝜖)). For the second component, this study proposes a general propagational reinforcement-learning-based learning search method that improves the estimate h(.) according to the newly observed distance information about the goals, propagates the improvement bidirectionally in the search tree, and consequently obtains a sequence of more accurate V-values for a sequence of states. Experiments on the maze problems show that our method increases the convergence rate of reinforcement learning by a factor of 2.06 and reduces the number of learning episodes to 1/4 that of other nonpropagating methods.

An Implementation of Othello Game Player Using ANN based Records Learning and Minimax Search Algorithm

An Implementation of Othello Game Player Using ANN based Records Learning and Minimax Search Algorithm

An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance

In this paper, first we present an educational system that assists students in learning and tutors in teaching search algorithms, an artificial intelligence topic. Learning is achieved through a wide range of learning activities. Algorithm visualizations demonstrate the operational functionality of algorithms according to the principles of active learning. So, a visualization process can stop and request from a student to specify the next step or explain the way that a decision was made by the algorithm. Similarly, interactive exercises assist students in learning to apply algorithms in a step-by-step interactive way. Students can apply an algorithm to an example case, specifying the algorithm’s steps interactively, with the system’s guidance and help, when necessary. Next, we present assessment approaches integrated in the system that aim to assist tutors in assessing the performance of students, reduce their marking task workload and provide immediate and meaningful feedback to students. Automatic assessment is achieved in four stages, which constitute a general assessment framework. First, the system calculates the similarity between the student’s and the correct answer using the edit distance metric. In the next stage, it identifies the type of the answer, based on an introduced answer categorization scheme related to completeness and accuracy of an answer, taking into account student carelessness too. Afterwards, the types of errors are identified, based on an introduced error categorization scheme. Finally, answer is automatically marked via an automated marker, based on its type, the edit distance and the type of errors made. To assess the learning effectiveness of the system an extended evaluation study was conducted in real class conditions. The experiment showed very encouraging results. Furthermore, to evaluate the performance of the assessment system, we compared the assessment mechanism against expert (human) tutors. A total of 400 students’ answers were assessed by three tutors and the results showed a very good agreement between the automatic assessment system and the tutors.

Learning Search Algorithms: An Educational View

Artificial intelligence methods find their practical usage in many applications including maritime industry. The paper concentrates on the methods of uninformed and informed search, potentially usable in solving of complex problems based on the state space representation. The problem of introducing the search algorithms to newcomers has its technical and psychological dimensions. The authors show how it is possible to cope with both of them through design and use of specialized authoring systems. A typical example of searching a path through the maze is used to demonstrate how to test, observe and compare properties of various search strategies. Performance of search methods is evaluated based on the common criteria.

Pricing strategy in a dual-channel and remanufacturing supply chain system

This article addresses the pricing strategy problems in a supply chain system where the manufacturer sells original products and remanufactured products via indirect retailer channels and direct Internet channels. Due to the complexity of that system, agent technologies that provide a new way for analysing complex systems are used for modelling. Meanwhile, in order to reduce the computational load of searching procedure for optimal prices and profits, a learning search algorithm is designed and implemented within the multi-agent supply chain model. The simulation results show that the proposed model can find out optimal prices of original products and remanufactured products in both channels, which lead to optimal profits of the manufacturer and the retailer. It is also found that the optimal profits are increased by introducing direct channel and remanufacturing. Furthermore, the effect of customer preference, direct channel cost and remanufactured unit cost on optimal prices and profits are examined.