• Publications
  • Influence
SPEA2: Improving the strength pareto evolutionary algorithm
TLDR
An improved version of SPEA, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. Expand
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
TLDR
The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Expand
Indicator-Based Selection in Multiobjective Search
TLDR
This paper proposes a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators and can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. Expand
Performance assessment of multiobjective optimizers: an analysis and review
TLDR
This study provides a rigorous analysis of the limitations underlying this type of quality assessment in multiobjective evolutionary algorithms and develops a mathematical framework which allows one to classify and discuss existing techniques. Expand
Scalable multi-objective optimization test problems
TLDR
Three different approaches for systematically designing test problems for systematic designing multi-objective evolutionary algorithms (MOEAs) showing efficacy in handling problems having more than two objectives are suggested. Expand
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TLDR
This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search. Expand
Evolutionary algorithms for multiobjective optimization: methods and applications
TLDR
The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Expand
Scalable Test Problems for Evolutionary Multiobjective Optimization
TLDR
Three different approaches for systematically designing test problems for systematically demonstrating the efficacy of multiobjective evolutionary algorithms in handling problems having more than two objectives are suggested. Expand
...
1
2
3
4
5
...