Peter J. Fleming

TL;DR: A rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs) and the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM.

TL;DR: Current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality.

TL;DR: In this article, a multiobjective genetic algorithm based on the proposed decision strategy is proposed, and a suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized and shown to encompass a number of simpler decision strategies.

TL;DR: In this paper, the important role of evolutionary algorithms in multi-objective optimisation is highlighted, and evolutionary advances in adaptive control and multidisciplinary design are predicted, as well as significant applications in parameter and structure optimisation for controller design and model identification, in addition to fault diagnosis, reliable systems, robustness analysis, and robot control.

TL;DR: This study illustrates how a technique such as the multiobjective genetic algorithm can be applied and exemplifies how design requirements can be refined as the algorithm runs, and demonstrates the need for preference articulation in cases where many and highly competing objectives lead to a nondominated set too large for a finite population to sample effectively.