Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Statistical methods for convergence detection of multi-objective evolutionary algorithms
Evolutionary Computation
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
A taxonomy of online stopping criteria for multi-objective evolutionary algorithms
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
On the effect of response transformations in sequential parameter optimization
Evolutionary Computation
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In recent years, new approaches for multi-modal and multiobjective stochastic optimisation have been developed. It is a rather normal process that these experimental fields develop independently from other scientific areas. However, the connection between stochastic optimisation and statistics is obvious and highly appreciated. Recent works, such as sequential parameter optimisation (SPO, cf. Bartz-Beielstein [1]) or online convergence detection (OCD, cf. Trautmann et al [2]), have combined methods from evolutionary computation and statistics. One important aspect in statistics is the analysis of stochastic outcomes of experiments and optimization methods, respectively. To this end, the optimisation runs of different evolutionary multi-objective optimisation algorithms (EMOA, cf. Deb [3] or Coello Coello et al. [4]) are treated as experiments to analyse the stochastic behavior of the results and to approximate the distribution of the performance of the EMOA. To combine the outcome of an EMOA and receive a single performance indicator value, the hypervolume (HV) indicator is considered, which is the only known unary quality indicator in this field (cf. Zitzler et al. [5]). The paper at hand investigates and compares the HV indicator outcome of multiple runs of two EMOA on different mathematical test cases.