Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Modelling of Design Decision for CAD
Computer Aided Design: Modelling, Systems Engineering, CAD-Systems - CREST Advanced Course
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Computer experiments and global optimization
Computer experiments and global optimization
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions
Journal of Global Optimization
On the Design of Optimization Strategies Based on Global Response Surface Approximation Models
Journal of Global Optimization
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiple objective optimisation applied to route planning
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
A popular approach to handling constraints in surrogate-based optimization is through the addition of penalty functions to an infill sampling criterion that seeks objective improvement. Typical sampling metrics, such as expected improvement tend to have multi modal landscapes and can be difficult to search. When the problem is transformed using a penalty approach the search can become riddled with cliffs and further increases the complexity of the landscape. Here we avoid searching this aggregated space by treating objective improvement and constraint satisfaction as separate goals, using multiobjective optimization. This approach is used to enhance the efficiency and reliability of infill sampling and shows some promising results. Further to this, by selecting model update points in close proximity to the constraint boundaries, the regions that are likely to contain the feasible optimum can be better modelled. The resulting enhanced probability of feasibility is used to encourage the exploitation of constraint boundaries.