A Nascent Technique for Cultivated Feature Selection using Evolutionary Computation Algorithms

Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence. Recently, with the surge in data-intensive applications and large-scale complex systems, the demand for scalable EC solutions has grown significantly. However, most existing EC infrastructures fall short of catering to the heightened demands of large-scale problem solving. While the advent of some pioneering GPU-accelerated EC libraries is a step forward, they also grapple with some limitations, particularly in terms of flexibility and architectural robustness. In response, we introduce EvoX: a computing framework tailored for automated, distributed, and heterogeneous execution of EC algorithms. At the core of EvoX lies a unique programming model to streamline the development of parallelizable EC algorithms, complemented by a computation model specifically optimized for distributed GPU acceleration. Building upon this foundation, we have crafted an extensive library comprising a wide spectrum of 50+ EC algorithms for both single-and multi-objective optimization. Furthermore, the library offers comprehensive support for a diverse set of benchmark problems, ranging from dozens of numerical test functions to hundreds of reinforcement learning tasks. Through extensive experiments across a range of problem scenarios and hardware configurations, EvoX demonstrates robust system and model performances. EvoX is open-source and accessible at: https://github.com/EMI-Group/EvoX.

As the volume of available remotely sensed imagery increases so does the need to extract more specific information in a timely and cost‐effective fashion to enhance and/or update decision support systems. This manuscript provides an overview of the existing image information extraction techniques using evolutionary computation algorithms. Emphasis is given to remote sensing applications. The literature investigated is further divided into four groups based on the research objectives: image enhancement, image classification, feature extraction, and modeling. Some of the strengths of evolutionary computation are discussed, such as robust classification capabilities and ability to work with linear and non‐linear problems. Conversely, the works reviewed reveal some of the challenges in the integration of evolutionary computation and remote sensing as well as possible solutions. Limited work was found addressing the multi‐class image classification problem and assessment of the randomness involved in the generation of the first set of candidate solutions from which the algorithm begins. Based on our study, the use of evolutionary computation algorithms for information extraction from imagery is likely to grow, specifically in two areas: inverse modeling and object‐based approaches. This technology is still in its early stages of development and the capabilities of evolutionary computation are well suited to the complexities of the image processing and information extraction problems.

We choose the well-known evolution strategy (ES) in the evolutionary computation (EC) community to solve the large-scale scheduling problem provided by Alibaba cloud services. Since the problem is accompanied by multiple strong constraints, we design two additional strategies for improving the search efficiency with a given limited computational cost. Compared with widely used numerical benchmark test suits, this problem arises from the requirements of real-world applications and has strict constraints that cannot be violated, such as processing time, response timeout, load balance, and so on. The main contribution of this paper is to establish a bridge between EC algorithms and the characteristics of real-world problems so that EC algorithms can solve real-world problems more effectively and smoothly. Based on the difficulties encountered in the experiment, we summarize some of our experiences and insights, and hope that they may bring new enlightenment to the latecomers.KeywordsEvolutionary computationEvolution strategyScheduling optimizationLarge-scale optimization

This work investigates the characterization and modelling of hysteresis in a core-free dielectric electro-active polymer (EAP) tubular actuator. The overall hysteresis effect of the voltage driven system comprises the inherent hysteresis of the fabricated tubular actuator plus a time lag introduced by the associated power supply when charging and discharging the actuator. Specifically the dynamic asymmetric hysteretic model of the voltage driven tubular actuator is decomposed into two models in series, comprising the nonlinear static voltage-strain characteristic of the actuator and an approximate symmetric hysteretic characteristic. The Bouc-Wen model approach is popular in engineering because of its simple interpretation as a nonlinear black-box model, the relatively low number of parameters needed to describe it, and the availability of both optimization and least squares estimation approaches to identify model parameters from experimental data. A disadvantage of the Bouc-Wen modelling approach is that it cannot accurately model asymmetric hysteresis behaviour. The use of the decomposition approach allows the Bouc-Wen model to be used to describe the approximate symmetric hysteretic characteristic. The model parameters are identified using an evolutionary computational algorithm - particle swarm optimization (PSO). PSO is an evolutionary based optimization approach that has been shown to be superior to genetic algorithms.

Application of evolutionary computation on ensemble forecast of quantitative precipitation

A comprehensive survey on interactive evolutionary computation in the first two decades of the 21st century

Composite particle optimization with hyper-reflection scheme in dynamic environments

A pharmaceutical supply chain (PSC) is a system of processes, operations, and organisations for drug delivery. This paper provides a new PSC mathematical cost model, which includes Blockchain technology (BT), that can improve the safety, performance, and transparency of medical information sharing in a healthcare system. We aim to estimate the costs of the BT-based PSC model, select algorithms with minimum prediction errors, and determine the cost components of the model. After the data generation, we applied four Supervised Learning algorithms (k-nearest neighbour, decision tree, support vector machine, and naive Bayes) combined with two Evolutionary Computation algorithms (ant colony optimization and the firefly algorithm). We also used the Feature Weighting approach to assign appropriate weights to all cost model components, revealing their importance. Four performance metrics were used to evaluate the cost model, and the total ranking score (TRS) was used to determine the most reliable predictive algorithms. Our findings show that the ACO-NB and FA-NB algorithms perform better than the other six algorithms in estimating the costs of the model with lower errors, whereas ACO-DT and FA-DT show the worst performance. The findings also indicate that the shortage cost, holding cost, and expired medication cost more strongly influence the cost model than other cost components.

Artificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust. In response, the field of explainable AI (XAI) has emerged to address the need for human-understandable AI systems. Evolutionary computation (EC), a family of powerful optimization and learning algorithms, offers significant potential to contribute to XAI, and vice versa. This article provides an introduction to XAI and reviews current techniques for explaining machine learning (ML) models. We then explore how EC can be leveraged in XAI and examine existing XAI approaches that incorporate EC techniques. Furthermore, we discuss the application of XAI principles within EC itself, investigating how these principles can illuminate the behavior and outcomes of EC algorithms, their (automatic) configuration, and the underlying problem landscapes they optimize. Finally, we discuss open challenges in XAI and highlight opportunities for future research at the intersection of XAI and EC. Our goal is to demonstrate EC’s suitability for addressing current explainability challenges and to encourage further exploration of these methods, ultimately contributing to the development of more understandable and trustworthy ML models and EC algorithms.

The broad field of evolutionary computation (EC)-including genetic algorithms as a special case-has attracted much attention in the last several decades. Many bold claims have been made about the effectiveness of various EC algorithms. These claims have centered on the efficiency, robustness, and ease of implementation of EC approaches. Unfortunately, there seems to be little theory to support such claims. One key step to formally evaluating or substantiating such claims is to establish rigorous results on the rate of convergence of EC algorithms. This paper presents a computable rate of convergence for a class of ECs that includes the standard genetic algorithm as a special case.

This study proposes a new hybrid algorithm for solving small to large-scale continuous global optimization problems. It comprises evolutionary computation algorithm featuring a novel adaptive elitism strategy and a sequential quadratic programming algorithm; combined in a collaborative portfolio with a validation procedure. The sequential quadratic programming is a gradient based local search method designed to derive effective search directions by using exact Hessians obtained via a vectorized forward accumulation of derivatives technique. The proposed hybrid design aim was to ensure that the two algorithms complement each other by effectively exploring and exploiting the problem search space. Experimental results justify that an adept hybridization of evolutionary algorithms with a suitable local search method could yield a robust and efficient means of solving wide range of global optimization problems.

Discusses an evolutionary computation (EC) approach to developing control laws for aircraft flight control systems. A generic control law structure is defined that incorporates the basic control elements that are normally used in flight control systems. A generic gain schedule algorithm is also defined that can be used to determine the gains for the control structure based on the flight conditions. These two elements are then integrated with a multiple-flight-condition linear aircraft model and an EC algorithm that searches for an optimum control law that is valid throughout the flight envelope. This approach eliminates the normal manual iterative development methodology that is used to develop aircraft control laws. It also provides a rapid prototyping capability that allows an existing flight control design to be customized for a different aircraft. Experimental results demonstrate the effectiveness of the proposed approach and compare the convergence speeds of various EC variations.

This paper has proposed a distributed power control algorithm that uses a stochastic search technique in order to solve linear systems of equations for power update in CDMA cellular radio systems. The proposed algorithm is developed by applying Bremermann's evolutionary computation algorithm to the CDMA power control problem. The major gain from the applied evolutionary computation algorithm is more rapid optimization on linear systems of equations compared with the simple genetic algorithm (SGA). Employing the distributed constrained power control (DCPC) as a reference algorithm, we have designed and implemented computational experiments on the DS-CDMA system. The results indicate that the proposed algorithm significantly enhances the optimization speed of power control. The proposed algorithm is also compared with the bang-bang type algorithm used in the IS-95 and the W-CDMA systems. The results show that our proposed algorithm also has a high potential advantage for increasing the CDMA cellular radio network capacity and decreasing the mobile terminal power consumption.

Protein structure prediction (PSP) problems are a major biocomputing challenge, owing to its scientific intrinsic that assists researchers to understand the relationship between amino acid sequences and protein structures, and to study the function of proteins. Although computational resources increased substantially over the last decade, a complete solution to PSP problems by computational methods has not yet been obtained. Using only one energy function is insufficient to characterize proteins because of their complexity. Diverse protein energy functions and evolutionary computation algorithms have been extensively studied to assist in the prediction of protein structures in different ways. Such algorithms are able to provide a better protein with less computational resources requirement than deep learning methods. For the first time, this study proposes a many-objective PSP (MaOPSP) problem with four types of objectives to alleviate the impact of imprecise energy functions for predicting protein structures. A many-objective evolutionary algorithm (MaOEA) is utilized to solve MaOPSP. The proposed method is compared with existing methods by examining 34 proteins. An analysis of the objectives demonstrates that our generated conformations are more reasonable than those generated by single/multiobjective optimization methods. Experimental results indicate that solving a PSP problem as an MaOPSP problem with four objectives yields better PSPs, in terms of both accuracy and efficiency. The source code of the proposed method can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://toyamaailab.github.io/sourcedata.html</uri> .

Optimal sink node positioning has the potential to prolong the lifetime of WSNs. There are many challenges facing optimal sink node positioning. These include an infinite solution space, the involvement of a huge number of sensor nodes, the different network architectures and routing protocols in use, different sensor node capabilities, and different application needs. This chapter categorizes the optimal sink node positioning problem in terms of the features of the sink node, i.e. static sink node, dynamic sink node, and mobile sink node. Multiple sink node positioning is also considered. Even though any optimization algorithms can be applied to the positioning of sink nodes, only evolutionary computation algorithms are illustrated in the chapter as a case study.