18 citations · 83 across the 20 of their papers we have counts for
21 papers
The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity
André H. Deutz, Michael T. M. Emmerich, Hao Wang
The problem of approximating the Pareto front of a multiobjective optimization problem can be reformulated as the problem of finding a set that maximizes the hypervolume indicator.…
Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications
Patrick Echtenbruck, Martina Echtenbruck, Joost Batenburg +3
Automated model selection is often proposed to users to choose which machine learning model (or method) to apply to a given regression task. In this paper, we show that combining d…
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
Hao Wang, Kaifeng Yang, Michael Affenzeller
Hypervolume improvement (HVI) is commonly employed in multi-objective Bayesian optimization algorithms to define acquisition functions due to its Pareto-compliant property. Rather…
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization
Yali Wang, Steffen Limmer, Markus Olhofer +2
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region. It is named Automatic Preference base…
Multiple Node Immunisation for Preventing Epidemics on Networks by Exact Multiobjective Optimisation of Cost and Shield-Value
Michael Emmerich, Joost Nibbeling, Marios Kefalas +1
The general problem in this paper is vertex (node) subset selection with the goal to contain an infection that spreads in a network. Instead of selecting the single most important…
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning
Hui Wang, Mike Preuss, Michael Emmerich +1
Morpion Solitaire is a popular single player game, performed with paper and pencil. Due to its large state space (on the order of the game of Go) traditional search algorithms, suc…
A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks
Divyam Aggarwal, Dhish Kumar Saxena, Saaju Pualose +2
Crew Pairing Optimization (CPO) is critical for an airlines' business viability, given that the crew operating cost is second only to the fuel cost. CPO aims at generating a set of…
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling
Yali Wang, Bas van Stein, Michael T. M. Emmerich +1
A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP). It uses smart initialization approaches…
Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones
Yali Wang, André Deutz, Thomas Bäck +1
Given a point in -dimensional objective space, any -ball of a point can be partitioned into the incomparable, the dominated and dominating region. The ratio between…
Airline Crew Pairing Optimization Framework for Large Networks with Multiple Crew Bases and Hub-and-Spoke Subnetworks
Divyam Aggarwal, Dhish Kumar Saxena, Thomas Bäck +1
Crew Pairing Optimization aims at generating a set of flight sequences (crew pairings), covering all flights in an airline's flight schedule, at minimum cost, while satisfying seve…
Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method
Divyam Aggarwal, Dhish Kumar Saxena, Thomas Back +1
Airline crew pairing optimization problem (CPOP) aims to find a set of flight sequences (crew pairings) that cover all flights in an airline's highly constrained flight schedule at…
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms
Kaifeng Yang, Michael Emmerich, André Deutz +1
In the field of multi-objective optimization algorithms, multi-objective Bayesian Global Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective op…
Hyper-Parameter Sweep on AlphaZero General
Hui Wang, Michael Emmerich, Mike Preuss +1
Since AlphaGo and AlphaGo Zero have achieved breakground successes in the game of Go, the programs have been generalized to solve other tasks. Subsequently, AlphaZero was developed…
Assessing the Potential of Classical Q-learning in General Game Playing
Hui Wang, Michael Emmerich, Aske Plaat
After the recent groundbreaking results of AlphaGo and AlphaZero, we have seen strong interests in deep reinforcement learning and artificial general intelligence (AGI) in game pla…
Maximum Volume Subset Selection for Anchored Boxes
Karl Bringmann, Sergio Cabello, Michael T. M. Emmerich
Let be a set of axis-parallel boxes in such that each box has a corner at the origin and the other corner in the positive quadrant of , and let…
Monte Carlo Q-learning for General Game Playing
Hui Wang, Michael Emmerich, Aske Plaat
After the recent groundbreaking results of AlphaGo, we have seen a strong interest in reinforcement learning in game playing. General Game Playing (GGP) provides a good testbed for…
Cluster-based Kriging Approximation Algorithms for Complexity Reduction
Bas van Stein, Hao Wang, Wojtek Kowalczyk +2
Kriging or Gaussian Process Regression is applied in many fields as a non-linear regression model as well as a surrogate model in the field of evolutionary computation. However, th…
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
Samineh Bagheri, Wolfgang Konen, Michael Emmerich +1
Constrained optimization of high-dimensional numerical problems plays an important role in many scientific and industrial applications. Function evaluations in many industrial appl…
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm
Jiaqi Zhao, Vitor Basto Fernandes, Licheng Jiao +5
Finding a good classifier is a multiobjective optimization problem with different error rates and the costs to be minimized. The receiver operating characteristic is widely used in…
Faster Computation of Expected Hypervolume Improvement
Iris Hupkens, Michael Emmerich, André Deutz
The expected improvement algorithm (or efficient global optimization) aims for global continuous optimization with a limited budget of black-box function evaluations. It is based o…
Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance
Pu Wang, Michael Emmerich, Rui Li +3
ROC is usually used to analyze the performance of classifiers in data mining. ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical ROC curve, and covers pot…