Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Comparing a coevolutionary genetic algorithm for multiobjective optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Quality Assessment of Pareto Set Approximations
Multiobjective Optimization
Conflict, harmony, and independence: relationships in evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Achieving balance between proximity and diversity in multi-objective evolutionary algorithm
Information Sciences: an International Journal
Local preference-inspired co-evolutionary algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
On finding well-spread pareto optimal solutions by preference-inspired co-evolutionary algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Iterated multi-swarm: a multi-swarm algorithm based on archiving methods
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
The simultaneous optimisation of four or more conflicting objectives is now recognised as a challenge for evolutionary algorithms seeking to obtain full representations of trade-off surfaces for the purposes of a posteriori decision-making. Whilst there is evidence that some approaches can outperform both random search and standard Paretobased methods, best-in-class algorithms have yet to be identified. We consider the concept of co-evolving a population of decision-maker preferences as a basis for determining the fitness of competing candidate solutions. The concept is realised using an existing co-evolutionary approach based on goal vectors. We compare this approach and a variant to three realistic alternatives, within a common optimiser framework. The empirical analysis follows current best practice in the field. As the number of objectives is increased, the preference-driven co-evolutionary approaches tend to outperform the alternatives, according to the hypervolume indicator, and so make a strong claim for further attention in many-objective studies.