Feb 2026· Annual Conference on Genetic and Evolutionary Computation· pp. 438-446· 0 citations· 31 references
Computer ScienceMathematics
TL;DR
This work introduces a suite of synthetic multi-objective test problems with analytically controlled Pareto fronts and deliberately uninformative objective values, designed to decouple algorithmic behaviour from problem structure, allowing bias induced purely by algorithmic operators and design choices to be observed.
Abstract
Structural bias (SB) refers to systematic preferences of an optimisation algorithm for particular regions of the search space that arise independently of the objective function. While SB has been studied extensively in single-objective optimisation, its role in multi-objective optimisation remains largely unexplored. This is problematic, as dominance relations, diversity preservation and Pareto-based selection mechanisms may introduce or amplify structural effects. In this paper, we extend the concept of structural bias to the multi-objective setting and propose a methodology to study it in isolation from fitness-driven guidance. We introduce a suite of synthetic multi-objective test problems with analytically controlled Pareto fronts and deliberately uninformative objective values. These problems are designed to decouple algorithmic behaviour from problem structure, allowing bias induced purely by algorithmic operators and design choices to be observed. The test suite covers a range of Pareto front shapes, densities and noise levels, enabling systematic analysis of different manifestations of structural bias. We discuss methodological challenges specific to the multi-objective case and outline how existing SB detection approaches can be adapted. This work provides a first step towards behaviour-based benchmarking of multi-objective optimisers, complementing performance-based evaluation and informing more robust algorithm design.
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
By employing a difficulty-aware weighting scheme, the approach biases aggregation toward higher-dimensional instances, enabling a more discriminative assessment of scalability, robustness, and performance.
Regina C. L. C. de Sousa, Dênis E. C. Vargas, Elizabeth F. Wanner et al.· Annual Conference on Genetic...· 0 citations
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
An objective-wise variable analysis method that first evaluates the sensitivity of each objective to all decision variables, and then comprehensively aggregates the sensitivity information across multiple objectives to estimate the overall importance of decision variables is proposed.
Chuanlong Ye, Fazhi He, Xiaoxin Gao et al.· Journal of King Saud Univers...· 0 citations
Multidisciplinary design optimisation (MDO) is a field of optimisation where problems are partitioned into a set of subproblems, or disciplines, with interactions between them. Multi-objective (MO)-MDO considers cases where multiple objectives exist at the problem or subproblem level. Of particular interest are MO-MDO architectures and methods for a posteriori decision making—in which the aim of the optimiser is to produce an approximation of the efficient set of Pareto optimal solutions. A variety of a posteriori MO-MDO methods have been proposed, many of which draw on concepts and tools from evolutionary computation and machine learning. However, these approaches have arisen across a fragmented set of literatures, and there is no unified guide available to support practitioners or steer the progressive development of new methods. This survey aims to provide such a unified perspective. The issue of characterising multi-objectivity in MO-MDO is discussed and a typology is introduced to identify the different methods in an accessible and straightforward way. The available benchmark problems for MO-MDO are also surveyed. Key issues and potential paths for future research in MO-MDO are identified and discussed.
Victoria Johnson, João A. Duro, V. Kadirkamanathan et al.· ACM Transactions on Evolutio...· 0 citations
The Pareto set and Pareto front of a continuous multiobjective optimization problem typically contain infinitely many solutions. Since dealing with infinite sets is impractical, they are commonly approximated by finite reference sets. In this way, the performance of multiobjective optimization algorithms can be assessed using quality indicators. However, the selection of a reference set depends on the intended goal, such as achieving a uniform distribution of solutions along the Pareto set or Pareto front, or optimizing a specific quality indicator. In this paper, we investigate and compare six strategies for reference set construction in constrained bi-objective problems from a recently proposed test problem generator. The approaches are evaluated with respect to multiple quality indicators and computational cost. The experiments are performed on test problems with irregular Pareto sets and fronts. From these experiments, we conclude that the choice of strategy should depend on the intended goal, with the approach that aims to maximize the hypervolume indicator value standing out as the best trade-off in terms of speed and indicator accuracies.
Luka Opravš, D. Brockhoff, T. Tušar· Annual Conference on Genetic...· 0 citations