Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm design, and adaptive decision-making. This survey reviews the bidirectional interaction between these two paradigms and examines how their complementary strengths can be leveraged in hybrid intelligent systems. First, we analyze how EC can enhance LLM-based systems through prompt optimization, hyperparameter tuning, and architecture search. Second, we review how LLMs can improve EC by supporting metaheuristic design, surrogate reasoning, adaptive operator control, and heuristic generation. We further discuss emerging co-adaptive frameworks in which LLMs and EC interact through iterative feedback loops. Beyond summarizing recent developments, the survey provides a structured perspective on interaction mechanisms, application patterns, and methodological challenges, including computational cost, reproducibility, interpretability, benchmarking, and generalization. The paper concludes by outlining open research questions and future directions for developing more robust, transparent, and scalable LLM-EC systems.
The performance of a solar photovoltaic (PV) module or array is significantly affected by partial shading conditions (PSC), which reduce the output power and shorten the lifespan of the PV system due to the formation of hotspots on the PV cells. These conditions lead to multiple peaks on the power-voltage (P–V) curve, which cannot be accurately tracked using classical maximum power point tracking (MPPT) methods. Consequently, metaheuristic-based optimization techniques are employed to locate the global maximum power point (GMPP). This paper addresses the challenges posed by PSC in a standalone PV system by implementing a recent optimization algorithm known as the tumoral angiogenesis optimizer (TAO). The proposed MPPT approach is validated under various partial shading scenarios and benchmarked against existing MPPT algorithms, including particle swarm optimization (PSO), horse herd optimization algorithm (HOA), salp swarm algorithm (SSA), and osprey optimization algorithm (OOA). Simulations are conducted using MATLAB R2021. The performance is evaluated in terms of output power, voltage, and duty cycle, demonstrating the TAO algorithm’s capability to rapidly and accurately track the GMPP. Moreover, the tracking efficiency, convergence time, and power oscillations of each MPPT method are analyzed. The proposed method achieves an average tracking efficiency of 99.5
Accurate and early prediction of crop yield is essential for agricultural management, economic planning, and market stability, particularly for high-value products such as wine. This study presents a multi-temporal modeling framework to predict grapevine yield (in kilograms) in the province of Cádiz, Spain, a region with a deep-rooted winemaking heritage. Using a 12-year dataset that includes historical harvest records, meteorological variables, and time series of remotely sensed vegetation indices, Machine Learning (ML) regression models were developed and evaluated at three key phenological stages: post-harvest (December), post-dormancy (March), and post-flowering (June). The methodology employs a rigorous Leave-One-Year-Out (LOYO) cross-validation approach to assess model performance in the context of short time series. Results show a progressive and significant improvement in prediction accuracy throughout the growing season: the Mean Absolute Percentage Error (MAPE) decreases from 13.9
Game-based learning has become an increasingly popular educational methodology due to its ability to enhance student interest and engagement. The aim of this study was to analyze the effect of game-based learning on motivation, self-efficacy, and academic performance in Natural Sciences learning. A systematic review and meta-analytic methodology was employed, following PRISMA guidelines. For this purpose, the databases consulted were Web of Science and Scopus, from which a total of 234 documents were retrieved and reduced to 15 studies after rigorously applying the established eligibility criteria. These studies were included in the systematic review and meta-analysis to ensure the validity and relevance of the meta-analytic findings. The meta-analytic results revealed a very strong and highly significant positive effect across all subgroups, benefiting the experimental groups (Z = 6.29; p < 0.00001). In conclusion, the implementation of game-based learning has a positive impact on motivation, self-efficacy, and academic performance in the teaching and learning of Natural Sciences content. Therefore, its incorporation into pedagogical practices represents an opportunity to strengthen student engagement and promote more meaningful learning.
One of the most accepted definitions of violence is based on the motivations underlying the behavior, differentiating between reactive and proactive violence. Furthermore, violence manifest across various contexts and interpersonal relationships. Our objectives were to explore different forms of adolescent violence (child-to-parent violence, peer violence and dating violence), examining their interrelationships and the predictive capacity of the reactive and proactive behavioral pattern in the different types of violence. The sample consisted of 2,124 adolescents (57.9
