Plateaus can be harder in multi-objective optimization

—In recent years a lot of progress has been made in understanding the behavior of evolutionary computation methods for single- and multi-objective problems. Our aim is to analyze the diversity mechanisms that are implicitly used in evolutionary algorithms for multi-objective problems by rigorous runtime analyses. We show that, even if the population size is small, the runtime can be exponential where corresponding single-objective problems are optimized within polynomial time. To illustrate this behavior we analyze a simple plateau function in a first step and extend our result to a class of instances of the well-known SETCOVER problem.

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Tobias Friedrich, Nils Hebbinghaus, Frank Neumann
• Keywords: Artificial Intelligence, CEC 2007, Evolutionary Computation Methods, Multi-objective Problems, Rigorous Runtime Analyses