Paper Info
Title
Preference-driven co-evolutionary algorithms show promise for many-objective optimisation
Abstract
The simultaneous optimisation of four or more conflicting objectives is now recognised as a challenge for evolutionary algorithms seeking to obtain full representations of trade-off surfaces for the purposes of a posteriori decision-making. Whilst there is evidence that some approaches can outperform both random search and standard Paretobased methods, best-in-class algorithms have yet to be identified. We consider the concept of co-evolving a population of decision-maker preferences as a basis for determining the fitness of competing candidate solutions. The concept is realised using an existing co-evolutionary approach based on goal vectors. We compare this approach and a variant to three realistic alternatives, within a common optimiser framework. The empirical analysis follows current best practice in the field. As the number of objectives is increased, the preference-driven co-evolutionary approaches tend to outperform the alternatives, according to the hypervolume indicator, and so make a strong claim for further attention in many-objective studies.
Year
DOI
Venue
2011
10.1007/978-3-642-19893-9_10
EMO
Keywords
Field
DocType
many-objective optimisation,decision-maker preference,candidate solution,preference-driven co-evolutionary algorithm,empirical analysis,common optimiser framework,conflicting objective,best-in-class algorithm,preference-driven co-evolutionary approach,current best practice,evolutionary algorithm,existing co-evolutionary approach,co evolution,decision maker,random search,best practice,comparative study
Population,Random search,Best practice,Evolutionary algorithm,A priori and a posteriori,Artificial intelligence,Machine learning,Mathematics
Conference
Volume
ISSN
Citations 
6576
0302-9743
30
PageRank 
References 
Authors
0.97
22
3
Name
Order
Citations
PageRank
Robin C. Purshouse162830.00
Cezar Jalbă2300.97
Peter J. Fleming33023475.23