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The superior performance of large-scale pre-trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT), has received increasing attention in both academic and industrial research and ...

Forests play a pivotal role in protecting the environment, preserving vital natural resources, and ultimately sustaining human life. However, the escalating occurrences of forest fires, whether of human origin or due to climate change, poses a ...

A dedicated board is designed, implemented, and installed in forests. An efficient routing algorithm, based on the shortest path, is employed in the proposed approach, using clustering and cluster heads to transmit data to gateways via the closest and ...

Several machine learning and deep learning algorithms have been presented to detect the criminal behaviours in a smart grid environment in recent studies because of many successful results. However, most learning algorithms for the electricity ...

The proposed algorithm first builds on deep neural networks, a meta‐learner for determining the weights of detection models for the construction of an ensemble detection algorithm and then uses a promising metaheuristic algorithm named search economics ...

Automated heuristic design (AHD) has gained considerable attention for its potential to automate the development of effective heuristics. The recent advent of large language models (LLMs) has paved a new avenue for AHD, with initial efforts ...

In the event of failures, it is essential that the distribution network can autonomously adjust its topology structure to satisfy the power supply requirements. Therefore, how to reconstruct the distribution network is crucial for the development ...

This study investigates the use of the GPT-4 Turbo, a large language model, to enhance the Self-Organizing Migrating Algorithm (SOMA), specifically its All to All variant (SOMA-ATA). Utilizing the model's extensive context capacity for iterative ...

In this study, we investigate the applicability, challenges, and effectiveness of the advanced large language model GPT 4 Turbo in enhancing the selected metaheuristic algorithm, which is Differential Evolution. Our research primarily examines whether ...

Measuring diversity in evolutionary algorithms presents a complex challenge, especially in optimization tasks with variable dimensionality. Current literature offers limited insights on effectively quantifying diversity under these conditions. This paper ...

Cartesian Genetic Programming (CGP) allows for the optimization of interpretable function representations. However, comprehending the vast and combinatorially complex search space inherent to CGP remains challenging, particularly because multiple ...

Learning classifier systems (LCSs) are a state-of-the-art methodology for developing rule-based machine learning by applying discovery algorithms and learning components. LCSs have become proficient at linking environmental features to describe simple ...

Software Defined Perimeter (SDP) is a zero-trust network-isolation defense technique which aims to limit security risks by giving dynamic account type assignments to network users. Despite SDP being proven as an effective defense strategy in various ...

We report on a case study application of metaheuristics with Argyll and Bute Health and Social Care Partnership in the West of Scotland. The Partnership maintains a fleet of pool vehicles that are available to service visits of staff to locations across ...

In the field of energy systems, the "WCCI(CEC)/GECCO 2024 Competition Evolutionary Computation in the Energy Domain: Optimal PV System Allocation" serves as a platform for evaluating and comparing various metaheuristic algorithms tailored to address ...

Emerging ML/AI hardware accelerators, like the 850,000 processor Cerebras Wafer-Scale Engine (WSE), hold great promise to scale up the capabilities of evolutionary computation. However, challenges remain in maintaining visibility into underlying ...

Evolutionary computation (EC), as a powerful optimization algorithm, has been applied across various domains. However, as the complexity of problems increases, the limitations of EC have become more apparent. The advent of large language models (LLMs) ...

This article for the Hot-off-the-Press track at GECCO 2024 summarizes recent work titled 'Evo-Panel: Dynamic Visualization Tool for Optimization Process', published in IEEE Transactions on Emerging Topics in Computational Intelligence [4]. We believe ...

Despite the popularity of evolutionary multi-objective (EMO) algorithms in practices, their theoretical foundations are still in the early development. Fundamental questions such as the benefits of crossover are not fully understood. This work provides a ...

This paper presents an extended abstract describing an entry into the benchmarking competition on a new GNBG-generated Test Suite. We are presenting the results of our previously published Distance based parameter adaptation for Success-History based ...

The Competition of Machine Learning for Evolutionary Computation for Solving Vehicle Routing Problems (ML4VRP) seeks to bring together machine learning and evolutionary computation communities to propose innovative techniques for vehicle routing problems ...

The optimal photovoltaic (PV) allocation problem is extremely challenging due to the nonlinearity, large solution space, inherent uncertainties, and system constraints of real-world power systems. Considering the complexity of this problem, two problem-...