Paper Info
Title
On finding well-spread pareto optimal solutions by preference-inspired co-evolutionary algorithm
Abstract
Preference-inspired co-evolutionary algorithm (PICEA) is a novel class of multi-objective evolutionary algorithm. In PICEA, the usual candidate solutions are guided toward the Pareto optimal front by co-evolving a set of decision maker preferences during the search process. PICEA-g is one realization of PICEAs in which goal vectors are taken as preferences. This study points out one limitation of this method -the obtained solutions are distributed unevenly along the Pareto optimal front. To handle this limitation, an improved fitness assignment method is proposed in which the density information of the solutions is considered. Experimental results, in terms of the selected performance metrics, show this improved fitness assignment method is effective.
Year
DOI
Venue
2013
10.1145/2463372.2463444
GECCO
Keywords
Field
DocType
decision maker preference,density information,well-spread pareto optimal solution,preference-inspired co-evolutionary algorithm,search process,multi-objective evolutionary algorithm,improved fitness assignment method,novel class,goal vector,pareto optimal front,multi objective optimization,co evolution,evolutionary algorithms
Mathematical optimization,Evolutionary algorithm,Computer science,Multi-objective optimization,Pareto optimal,Artificial intelligence,Fitness assignment,Machine learning,Decision maker
Conference
Citations 
PageRank 
References 
7
0.43
15
Authors
3
Name
Order
Citations
PageRank
Rui Wang153833.29
Robin C. Purshouse262830.00
Peter J. Fleming33023475.23