Jul 2026· Annual Conference on Genetic and Evolutionary Computation· pp. 215-223· 0 citations· 22 references
Computer Science
TL;DR
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.
Abstract
The Algorithm Selection Problem (ASP) aims to identify the most suitable algorithm for a given problem instance. This paper introduces MODT-ASP, a multi-objective evolutionary framework designed to construct interpretable decision trees for ASP. The proposed approach overcomes the limitations of existing methods, such as scalability constraints and the challenge of balancing competing objectives, by employing a customized encoding scheme and specialized genetic operators. These components effectively explore trade-offs between predictive accuracy and model complexity via Pareto optimization. An enhanced version, EMODT-ASP, further integrates refined control mechanisms to improve generalization. Comprehensive experimental evaluations demonstrate the robustness and effectiveness of the proposed framework. In a large-scale linear programming benchmark comprising 1,004 problems and 532 algorithms, MODT-ASP consistently produces high-quality Pareto-optimal solutions. Furthermore, in the Open Algorithm Selection Challenge (OASC), evaluated across 8 heterogeneous scenarios, the proposed approach achieves third place overall, outperforming 6 of 8 OASC competitors and all 3 IP+VND variants reported in the recent literature. These results confirm its strong cross-domain applicability.
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.
Evgueni Blanquart, L. Jourdan, Nadarajen Veerapen· Proceedings of the Genetic a...· 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
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.
This work investigates how balancing the number of decomposed subproblems and ants impacts performance under a fixed computational budget and introduces MOEA/D-AM, a hybrid algorithm that adapts this decomposition strategy for partial classification rule induction.
Adel Abdelkader Mokadem, Marie-Éléonore Kessaci, Laetitia Jourdan et al.· Proceedings of the Genetic a...· 0 citations
Structural optimization problems often involve a large number of decision variables and highly non-convex feasible regions, making convergence to the true Pareto front extremely challenging. Even when convergence is achievable, it typically requires thousands of function evaluations, resulting in significant computational cost. This highlights the need for efficient and robust optimization algorithms for real-world engineering applications. In this study, we introduce a novel constrained multi-objective evolutionary algorithm, termed DPCME. The algorithm employs two interacting populations that exchange information, enabling effective global exploration and reducing the risk of convergence to local optima. To further enhance performance, a recent repair-based constraint-handling technique is incorporated, and alternative repair approaches are proposed and systematically evaluated. The proposed algorithm is tested on three engineering problems: the 72-bar truss, the 120-bar truss, and a chemical tanker structure, each involving hundreds of nonlinear failure constraints. Its performance is evaluated against state-of-the-art constrained multi-objective optimization algorithms from the latest PlatEMO package. A total of 43 algorithms are initially tested, from which the 12 best-performing methods are selected for detailed comparison. The results demonstrate that DPCME achieves superior or competitive convergence and diversity across all test cases, and that the inclusion of repair-based constraint handling further improves its performance.
Mixed-integer optimization (MIO) involves solving a problem defined by both discrete and continuous decision variables. Although common in real-world applications, MIO is difficult to solve because traditional metaheuristics are primarily designed for a single variable type. This paper compares six different strategies identified in a recent survey, categorizing them as either global strategies (GS), which treat the decision vector as a unified type, or decomposition-based strategies (DS), which split the problem into continuous and discrete subproblems. We benchmark these strategies based on a genetic algorithm across the 24 functions of the COCO BBOB-mixint test suite. The results show that GS are more efficient at performing simple functions and are more robust in multi-modal landscapes. As expected, however, DS are vulnerable to variable interdependencies. A resolution limit has been identified for high conditioning functions, whereby all strategies reach a performance ceiling. This is partly because the landscape features are smaller than the minimum integer step size of 1, causing the algorithm to exceed the optimal level. These results suggest that future research should explore hybrid metaheuristics to overcome the inherent constraints of the integer grid.
Thomas Lang, Denis Pallez· Proceedings of the Genetic a...· 0 citations