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Found 18 papers, 4 papers with code
Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications
More specifically, in this paper, a heuristic weight optimization, used in a preceding conference paper, is replaced by an exact optimization algorithm using convex quadratic programming.
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
This work provides the exact expression of the probability distribution of the hypervolume improvement (HVI) for bi-objective generalization of Bayesian optimization.
Efficient Stochastic Simulation of Network Topology Effects on the Peak Number of Infections in Epidemic Outbreaks
As a model and simulation method, we develop a continuous-time Markov chain (CTMC) model and an efficient simulation-based on Gillespie's Stochastic Simulation Algorithm (SSA).
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region.
Multiple Node Immunisation for Preventing Epidemics on Networks by Exact Multiobjective Optimisation of Cost and Shield-Value
The general problem in this paper is vertex (node) subset selection with the goal to contain an infection that spreads in a network.
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning
A later algorithm, Nested Rollout Policy Adaptation, was able to find a new record of 82 steps, albeit with large computational resources.
A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks
Crew Pairing Optimization (CPO) is critical for an airlines' business viability, given that the crew operating cost is second only to the fuel cost.
Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones
Given a point in $m$-dimensional objective space, any $\varepsilon$-ball of a point can be partitioned into the incomparable, the dominated and dominating region.
On Initializing Airline Crew Pairing Optimization for Large-scale Complex Flight Networks
Even generating an initial feasible solution (IFS: a manageable set of legal pairings covering all flights), which could be subsequently optimized is a difficult (NP-complete) problem.
Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?
A secondary result of our experiments concerns the choice of optimization goals, for which we also provide recommendations.
Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method
In a significant departure, this paper considers over 800 flights of a US-based large airline (with a monthly network of over 33, 000 flights), and tests the efficacy of GAs by enumerating all 400, 000+ crew pairings, apriori.
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms
In this paper, an efficient algorithm for the computation of the exact EHVI for a generic case is proposed.
Hyper-Parameter Sweep on AlphaZero General
Therefore, in this paper, we choose 12 parameters in AlphaZero and evaluate how these parameters contribute to training.
Assessing the Potential of Classical Q-learning in General Game Playing
For small games, simple classical table-based Q-learning might still be the algorithm of choice.
Monte Carlo Q-learning for General Game Playing
GGP problems can be solved by reinforcement learning.
Cluster-based Kriging Approximation Algorithms for Complexity Reduction
In addition, four Kriging approximation algorithms are proposed as candidate algorithms within the new framework.
An Ontology of Preference-Based Multiobjective Metaheuristics
User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization.
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
We analyze the importance of the several new elements in SACOBRA and find that each element of SACOBRA plays a role to boost up the overall optimization performance.
