Jul 2026· Proceedings of the Genetic and Evolutionary Computation Conference Companion· 0 citations· 15 references
TL;DR
This work investigates the hybridization of a Genetic Algorithm with Pareto Local Search to improve the exploration of non-dominated rulesets and provides a documented baseline for hybrid metaheuristics in rule mining.
Abstract
Rule-based classifiers offer high interpretability, which is essential in domains such as medicine where decision transparency is key. However, multi-objective rule learning remains a complex challenge due to the highly combinatorial nature of the search space. This work investigates the hybridization of a Genetic Algorithm (GA) with Pareto Local Search (PLS) to improve the exploration of non-dominated rulesets. Built upon the MH-Builder meta-heuristics framework, and the existing Multi-Objective Classification Algorithm for Imbalanced Data, or MOCA-I, our proposed pipeline uses a GA phase for global exploration followed by an Iterated PLS phase for intensification. Our results demonstrate that while the hybrid MOCA-I+GA pipeline maintains the same high predictive performance as MOCA-I, the evolutionary phase provides only marginal improvements in diversity (Hypervolume and Spread) at a significant computational cost. This study provides a documented baseline for hybrid metaheuristics in rule mining and discusses the limitations of loose-coupled sequential hybridization.
MODT-ASP, a multi-objective evolutionary framework designed to construct interpretable decision trees for ASP overcomes the limitations of existing methods, by employing a customized encoding scheme and specialized genetic operators to explore trade-offs between predictive accuracy and model complexity via Pareto optimization.
Matheus Guedes Vilas Boas, E. Wanner, Gladston J. P. Moreira· Annual Conference on Genetic...· 0 citations
Large Language Models (LLMs) are opening new directions for automated heuristic design (AHD), allowing evolutionary methods to create and enhance heuristics for constrained optimization problems (COPs). However, most existing approaches face the challenge of the exploration-exploitation balance, where the evolution needs to escape convergence to homogeneous populations and discover as large a heuristic landscape as possible. To address this challenge, we introduce Quality-Diversity Evolution (QDEvo), a multi-objective framework that integrates Quality-Diversity optimization with LLM-based AHD. At its core, QDEvo employs a semantic survival selection mechanism that clusters algorithms by functional similarity, then applies local Pareto competition. Evaluation on well-known COPs benchmarks and real-world problems shows that our method consistently outperforms the state-of-the-art baseline in both Hypervolume and Inverted Generational Distance metrics. These results facilitate further exploration of the algorithmic design space, while ensuring competitive solution quality and efficiency.
Nam Do Khanh, Nhat Nguyen Tran Minh, Dat Pham Vu Tuan et al.· Proceedings of the Genetic a...· 1 citation
This study systematically investigates multiple elitism mechanisms for NT within tree-based MO Genetic Programming (MOGP), including NSGA-II population replacement, crowding distance, first-objective, and a novel ideal-point strategy, comparing them against non-elitist NT and standard NSGA-II.
Filipa Vieira Goncalves Pereira, Karina Brotto Rebuli, M. Giacobini et al.· Proceedings of the Genetic a...· 0 citations
: Feature selection (FS) plays a crucial role in machine learning by reducing data dimensionality and improving learning efficiency. In many real-world scenarios, label information is unavailable, making unsupervised FS particularly important. While Genetic Algorithm (GA) offers a powerful global search mechanism for subset selection, it often suffers from premature convergence and struggles to refine solutions in complex search spaces. To address these limitations, we propose a hybrid GA that integrates an information-theoretic local search strategy for unsupervised FS. The proposed method integrates an information-theoretic local refinement procedure, consisting of DEL and ADD operations based on joint entropy, into a conventional GA framework. Unlike conventional evolutionary methods, our approach leverages information-theoretic measures not merely for evaluation, but as a guiding mechanism for fine-grained local exploration within the GA framework. By incorporating mutual information-based local refinement, the proposed method effectively overcomes the convergence bottlenecks of standard GAs, ensuring a more robust exploitation of feature dependencies. Experimental results on five datasets demonstrate that the proposed method consistently achieves higher clustering performance compared with conventional methods. These results imply that the proposed information-theoretic local refinement effectively mitigates the premature convergence problem of conventional GAs and improves search efficiency and solution quality compared to traditional heuristic and evolutionary approaches. It provides a promising framework for handling high-dimensional data in scenarios where label information is unavailable.
Seyeon Son, Hyunki Lim· Computers, Materials & C...· 0 citations
Multi-objective optimization provides the mathematical foundation for reasoning about trade-offs in complex decision problems, from engineering design to resource allocation. Software testing exemplifies such problems: practitioners must simultaneously optimize for fault detection capability, code coverage, execution cost, and test suite diversity—objectives that are fundamentally incommensurable. Since the early 2000s, multi-objective evolutionary algorithms (MOEAs) such as NSGA-II, MOEA/D, and their many-objective extensions (MOSA; DynaMOSA) have served as the dominant mathematical framework for navigating these trade-offs through Pareto-front approximation with hand-crafted fitness functions. However, the recent emergence of reinforcement learning (RL) and large language models (LLMs) is shifting the optimization paradigm from numerical Pareto-front approximation toward neural, semantically aware decision making over learned representations. This paper presents a systematic mapping study of multi-objective optimization algorithms, tracing their evolution from classical Pareto-based methods toward AI-driven and hybrid approaches, with software testing as the primary application domain. We survey 120+ papers published from 2000 to 2025 and propose a novel five-level taxonomy (L1–L5) that classifies optimization approaches along the intelligence spectrum: classical MOEAs, ML-guided MOEAs, RL-driven optimization, LLM-driven optimization, and hybrid neuro-evolutionary systems. For each level, we analyze the mathematical problem formulations (Pareto optimality conditions, Markov decision processes, and neural loss landscapes), objective function design, algorithmic convergence properties, and computational complexity. We further conduct a cross-cutting mathematical analysis comparing these paradigms along dimensions of convergence, diversity, scalability, and interpretability. Our survey identifies critical open mathematical challenges: the lack of formal convergence guarantees for LLM-driven optimization, the under-exploration of many-objective (m≥4) formulations in AI-driven testing, the sample complexity of reinforcement learning for combinatorial test optimization, and the absence of standardized benchmarks with known Pareto-optimal frontiers. We conclude by outlining a research roadmap for the next generation of multi-objective optimization systems that combine the complementary mathematical strengths of neural function approximation and evolutionary diversity preservation.
Support Vector Regression (SVR) is a reliable and generalizable model for non-linear regression, but its performance depends strongly on selecting proper hyperparameters such as the penalty parameter $C$, kernel width $\gamma$, and the $\epsilon$-insensitive loss. Because the SVR objective is highly nonconvex with a multimodal error surface, manual tuning and grid search become inefficient. Metaheuristic optimizers therefore offer a more practical alternative through population-based search. Although Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) have both shown promise, each has clear limitations: PSO converges quickly but often stagnates in local optima due to loss of diversity, whereas GA preserves diversity but converges slowly because of excessive random exploration. To address these complementary weaknesses, this work introduces a mathematically formulated hybrid framework called Adaptive PSO-GA (A-PSO-GA). Experiments on the Housing, Concrete, and Abalone benchmarks show that A-PSO-GA consistently outperforms PSO-only, GA-only, and non-adaptive hybrid baselines in RMSE, MAE, MAPE, and $R^{2}$. It also achieves faster and more stable convergence across multiple seeds, demonstrating improved prediction accuracy, robustness, and optimization reliability. These results confirm that mathematically justified adaptive mechanisms are important for effective evolutionary hyperparameter tuning of SVR.
Kumari Nidhi Lal, Yash Kumar, Mehtab Singh Rathore et al.· 2026 6th International Confe...· 0 citations