Evolutionary Multi-Criterion Optimization

9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings

Häftad, Engelska, 2017

AvHeike Trautmann,Günter Rudolph,Kathrin Klamroth,Oliver Schütze,Margaret Wiecek,Yaochu Jin,Christian Grimme,Gunter Rudolph,Oliver Schutze

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This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Münster, Germany in March 2017. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; parallel EMO models; EMO algorithm implementations.

Produktinformation

Utgivningsdatum2017-02-19
Mått155 x 235 x 39 mm
Vikt1 066 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor702
FörlagSpringer International Publishing AG
ISBN9783319541563

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On the effect of scalarising norm choice in a ParEGO implementation.- Multi-objective big data optimization with Metal and Spark.- An empirical assessment of the properties of inverted generational distance indicators on multi- and many-objective optimization.- Solving the Bi-objective traveling thief problem with multi-objective evolutionary algorithms.- Automatically Configuring multi-objective local search using multi-objective optimization.- The multi-objective shortest path problem is NP-hard, or is it.- Angle-based preference models in multi-objective optimization.- Quantitative performance assessment of multi-objective optimizers: The average runtime attainment function.- A multi-objective strategy to allocate roadside units in a vehicular network with guaranteed levels of service.- An approach for the local exploration of discrete many objective optimization problems.- A note on the detection of outliers in a binary outranking relation.- Classifying meta-modeling methodologiesfor evolutionary multi-objective optimization: First results.- Weighted stress function method for multi-objective evolutionary algorithm based on decomposition.- Timing the decision support for real-world many-objective problems.- On the influence of altering the action set on PROMETHEE II's relative ranks.- Peek { Shape { Grab: a methodology in three stages for approximating the non-dominated points of multi-objective discrete combinatorial optimization problems with a multi-objective meta-heuristic.- A new reduced-length genetic representation for evolutionary multi-objective clustering.- A fast incremental BSP tree archive for non-dominated points.- Adaptive operator selection for many-objective optimization with NSGA-III.- On using decision maker preferences with ParEGO.- First investigations on noisy model-based multi-objective optimization.- Fusion of many-objective non-dominated solutions using reference points.- An expedition to multi-modal multi-objective optimization landscapes.- Neutral neighbors in Bi-objective optimization: Distribution of the most promising for permutation problems.- Multi-objective adaptation of a parameterized GVGAI agent towards several games.- Towards standardized and seamless integration of expert knowledge into multi-objective evolutionary optimization algorithms.- Empirical investigations of reference point based methods when facing a massively large number of objectives: First results.- Building and using an ontology of preference-based multi-objective evolutionary algorithms.- A fitness landscape analysis of pareto local search on Bi-objective permutation flow-shop scheduling problems.- Dimensionality reduction approach for many-objective vehicle routing problem with demand responsive transport.- Heterogeneous evolutionary swarms with partial redundancy solving multi-objective tasks.- Multiple meta-models for robustness estimation in multi-objective robust optimization.- Predator-Prey techniques for solving multi-objective scheduling problems for unrelated parallel machines.- An overview of weighted and unconstrained scalarizing functions.- Multi-objective representation setups for deformation-based design optimization.- Design perspectives of an evolutionary process for multi-objective molecular optimization.- Towards a better balance of diversity and convergence in NSGA-III: First results.- A comparative study of fast adaptive preference-guided evolutionary multi-objective optimization.- A population-based algorithm for learning a majority rule sorting model with coalitional veto.- Injection of extreme points in evolutionary multio-objective optimization algorithms.- The impact of population size, number of children, and number of reference points on the performance of NSGA-III.- Multi-objective optimization for liner shipping fleet repositioning.- Surrogate-assisted partial order-based evolutionary optimization.- Hyper-volume indicator gradient ascent multi-objective optimization.- Toward step-size adaptation in evolutionary multi-objective optimization.- Computing 3-D expected hyper-volume improvement and related integrals in asymptotically optimal time.