Adaptive Multi-Objective Genetic Algorithm using Multi-Pareto-Ranking

Abstract

This paper extends an elitist multi-objective evolutionary algorithm, 
named GAME, based on several Pareto fronts corresponding to various 
fitness definitions. An additional operator is defined to create an adaptive 
version of this algorithm, called aGAME. This new operator alternates 
different modes of exploration of the search place all through aGAME 
execution. Mode switching is controlled according to the values of two 
performance indicators, in order to maintain a good compromise between 
the quality and diversity of the returned solutions. aGAME is compared with 
the previous version (GAME) and with the three best-ranking algorithms of 
the CEC 2009 competition, using five bi-objective benchmarks and the rules 
of this competition. This experimental comparison shows that aGAME outperforms 
these four algorithms, which validates both the efficiency of the proposed dynamic 
adaptive operator and the algorithm performance.