Set-based multi-objective optimization, indicators, and deteriorative cycles

Evolutionary multi-objective optimization deals with the task of computing a minimal set of search points according to a given set of objective functions. The task has been made explicit in a recent paper by Zitzler et al. [13]. We take an order-theoretic view on this task and examine how the use of indicator functions can help to direct the search towards Pareto optimal sets. Thereby, we point out that evolutionary algorithms for multi-objective optimization working on the dominance relation of search points have to deal with a cyclic behavior that may lead to worsenings with respect to the Pareto-dominance relation defined on sets. Later on, we point out in which situations well-known binary and unary indicators can help to avoid this cyclic behavior. Categories and Subject Descriptors F.2 [Theory of Computation]: Analysis of Algorithms and Problem Complexity General Terms Theory, Algorithms, Measurement, Performance Keywords Multiobjective Optimization, Performance Measures, Hyper...

• Type: Journal
• Year: 2010
• Where: GECCO
• Authors: Rudolf Berghammer, Tobias Friedrich, Frank Neumann
• Keywords: Evolutionary Multi-objective Optimization, GECCO 2010, Multi-objective Optimization, Optimization, Search Points