Name
Title | ||
|---|---|---|
A framework for incorporating trade-off information using multi-objective evolutionary algorithms |
Abstract | ||
|---|---|---|
Since their inception, multi-objective evolutionary algorithms have been adequately applied in finding a diverse approximation of efficient fronts of multi-objective optimization problems. In contrast, if we look at the rich history of classical multi-objective algorithms, we find that incorporation of user preferences has always been a major thrust of research. In this paper, we provide a general structure for incorporating preference information using multi-objective evolutionary algorithms. This is done in an NSGA-II scheme and by considering trade-off based preferences that come from so called proper Pareto-optimal solutions. We argue that finding proper Pareto-optimal solutions requires a set to compare with and hence, population based approaches should be a natural choice.Moreover, we suggest some practical modifications to the classical notion of proper Pareto-optimality. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of multi-objective evolutionary algorithms in finding the complete preferred region for a large class of complex problems. We also discuss a theoretical justification for our NSGA-II based framework. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15871-1_14 | PPSN (2) |
Keywords | Field | DocType |
multi-objective optimization problem,nsga-ii scheme,complete preferred region,proper pareto-optimal solution,proper pareto-optimality,multi-objective evolutionary algorithm,trade-off information,classical notion,computational study,complex problem,classical multi-objective algorithm | Population,Mathematical optimization,Inverted generational distance,Evolutionary algorithm,Computer science,Evolutionary computation,Trade-off,Artificial intelligence,Thrust,Optimization problem,Machine learning,Complex problems | Conference |
Volume | ISSN | ISBN |
6239 | 0302-9743 | 3-642-15870-6 |
Citations | PageRank | References |
14 | 0.81 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pradyumn Kumar Shukla | 1 | 274 | 23.97 |
| Christian Hirsch | 2 | 31 | 3.02 |
| Hartmut Schmeck | 3 | 1034 | 120.58 |