New Archive Based Evolutionary Multi-Objective Algorithms

By Xavier Esquivel

In this work we deal with the design of archive based multi-objective evolutionary algorithms (MOEAs) for the numerical treatment of multi objective optimization problems (MOPs). In particular, we design two generational operators- one mutation and one crossover operator that are tailored to a class of archiving strategies and propose a new evolutionary strategy. Furthermore, we investigate here two widely used indicators for the evaluation of Multi-objective Evolutionary Algorithms, the Generational Distance (GD) and the Inverted Generational Distance (IGD), with respect to the properties of ametric. We define a new performance indicator, ∆p, which can be viewed as an 'averaged Hausdorff distance' between the outcome set and the Pareto front and which is composed of (slight modifications of) the well-known indicators Generational Distance (GD) and Inverted Generational Distance (IGD). We will discuss theoretical properties of ∆p (as well as for GD and IGD) such as the metric properties and the compliance with state-of-the-art multi-objective evolutionary algorithms (MOEAs).

• Format: Paperback
• Language: English
• ISBN: 3659184969
• ISBN13: 9783659184963
• Release Date: July 2012
• Publisher: LAP Lambert Academic Publishing
• Length: 124 Pages
• Weight: 0.42 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"