Multiobjective evolutionary algorithm test suites
Proceedings of the 1999 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Capabilities of EMOA to detect and preserve equivalent pareto subsets
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Application of multiattribute decision analysis to quality functiondeployment for target setting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Tailoring ε-MOEA to concept-based problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Dynamic index tracking via multi-objective evolutionary algorithm
Applied Soft Computing
Hi-index | 0.00 |
In contrast to traditional multi-objective problems the concept-based version of such problems involves sets of particular solutions, which represent predefined conceptual solutions. This paper addresses the concept-based multi-objective problem by proposing two novel multi objective evolutionary algorithms. It also compares two major search approaches.The suggested algorithms deal with resource sharing among concepts, and within each concept, while simultaneously evolving concepts towards a Pareto front by way of their representing sets. The introduced algorithms, which use a simultaneous search approach, are compared with a sequential one. For this purpose concept-based performance indicators are suggested and used. The comparison study includes both the computational time and the quality of the concept-based front representation. Finally, the effect on the computational time of both the concept fitness evaluation time and concept optimality, for both the sequential and simultaneous approaches, is highlighted.