Abstract
The use of nondominance in a multi-objective search has traditionally focused on generating the set of nondominated solutions and choosing an element of this set in the implementation phase of the problem. This choice corresponds to the optimization of a single objective that relates to some extant utility. It ignores the fact that a multiobjective search can be context-dependent and has a rich nondominated set of solutions when the objectives represent complexity measures. In this article, we will present the concept of Pareto operating curves (POC). Often, systems operate along a POC based on risk, complexity. and tradeoffs that need to be made in response to changing environmental conditions. The key features of such systems are robustness and the ability to adapt to different environments.
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This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009
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Abbass, H.A., Bender, A. The Pareto operating curve for risk minimization. Artif Life Robotics 14, 449–452 (2009). https://doi.org/10.1007/s10015-009-0739-1
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DOI: https://doi.org/10.1007/s10015-009-0739-1