Titlebook: Evolutionary Multi-Criterion Optimization; 8th International Co António Gaspar-Cunha,Carlos Henggeler Antunes,Carl Conference proceedings 2

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書(shū)目名稱(chēng)Evolutionary Multi-Criterion Optimization影響因子(影響力)




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書(shū)目名稱(chēng)Evolutionary Multi-Criterion Optimization被引頻次




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書(shū)目名稱(chēng)Evolutionary Multi-Criterion Optimization讀者反饋




書(shū)目名稱(chēng)Evolutionary Multi-Criterion Optimization讀者反饋學(xué)科排名

現(xiàn)代

An Optimality Theory Based Proximity Measure for Evolutionary Multi-Objective and Many-Objective Option. To evaluate these algorithms, performance metrics either require the knowledge of the true Pareto-optimal solutions or, are ad-hoc and heuristic based. In this paper, we suggest a KKT proximity measure (KKTPM) that can provide an estimate of the proximity of a set of trade-off solutions from th

ASSET

極小量

Clustering Based Parallel Many-Objective Evolutionary Algorithms Using the Shape of the Objective Ve MOEAs are unable to maintain the same effectiveness showed for two or three objectives. Therefore, as a way to ameliorate this performance degradation several authors proposed preference-based methods as an alternative to Pareto based approaches. On the other hand, parallelization has shown to be u

linguistics

Faster Exact Algorithms for Computing Expected Hypervolume Improvementaussian distribution of a new candidate point. It is frequently used as an infill or prescreening criterion in multiobjective optimization with expensive function evaluations where predictions are provided by Kriging or Gaussian process surrogate models. The expected hypervolume improvement has good

MENT

A GPU-Based Algorithm for a Faster Hypervolume Contribution Computationot only as a quality measure for comparing final results of multi-objective evolutionary algorithms (MOEAs), but also as a selection operator (it is, for example, very suitable for .). However, it has one serious drawback: computing the exact hypervolume is highly costly. The best known algorithms t

MENT

A Feature-Based Performance Analysis in Evolutionary Multiobjective Optimizationatorial optimization, where a strict theoretical analysis is generally out of reach due to the high complexity of the underlying problem. Based on the examination of problem features from a multiobjective perspective, we improve the understanding of the efficiency of a simple dominance-based EMO alg

SKIFF

Modified Distance Calculation in Generational Distance and Inverted Generational Distancermance indicators evaluate the quality of an obtained solution set in comparison with a pre-specified reference point set. Both indicators are based on the distance between a solution and a reference point. The Euclidean distance in an objective space is usually used for distance calculation. Our id

Feigned

On the Behavior of Stochastic Local Search Within Parameter Dependent MOPsmulti-objective optimization problems. The discussions and initial computations indicate that the problem to compute an approximation of the entire solution set of such a problem via stochastic search algorithms is well-conditioned. The new insights may be helpful for the design of novel stochastic

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