Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Concept-based multi-objective problems and their solution by EC
Proceedings of the 9th annual conference companion on Genetic and 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
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This study is motivated by the need to support concept selection under conflicting objectives. A recent idea concerning concept-based relaxed-Pareto-optimality is employed to develop a "soft" evolutionary search approach. The proposed method allows set-based conceptual solutions, with performances close to those of the concept-based Pareto-optimal set, to survive the evolutionary search process. This allows designers, which are engaged in concept selection to examine not only the Pareto-optimal solutions from the different concepts. The relaxed-optimality exposes, within a desired performance resolution, other particular solutions of interest in concept selection. The proposed numerical solution approach involves a modification of NSGA-II to meet the needs of solving the described problem. The suggested algorithm is demonstrated using both an academic test function and a conceptual path planning problem.