Comparison of multi-objective evolutionary algorithms in optimizing combinations of reinsurance contracts

Our paper concerns optimal combinations of different types of reinsurance contracts. We introduce a novel approach based on the Mean-Variance-Criterion to solve this task. Two state-of-the-art MOEAs are used to perform an optimization of yet unresolved problem instances. In addition to that, we focus on finding a dense set of solutions to derive analogies to theoretic results of easier problem instances. Categories and Subject Descriptors I.2.1 [Artificial Intelligence]: Applications and Expert Systems; I.2.8 [Artificial Intelligence]: Problem Solving, Heuristic methods General Terms Algorithms, Design, Experimentation, Performance Keywords Multi-Objective Evolutionary Algorithm, Optimal Reinsurance, Mean-Variance-Criterion, Value-at-Risk

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Ingo Oesterreicher, Andreas Mitschele, Frank Schlottmann, Detlef Seese
• Keywords: Concerns Optimal Combinations, GECCO 2006, Optimization, Problem Instances, Unresolved Problem Instances