36 citations · 143 across the 10 of their papers we have counts for
10 papers
Solving Highly Constrained Multi-Objective Decision Problems with the IMAP-IGS Preference-Guided Metaheuristic Framework
L. J. K. Timp, N. Yorke-Smith, A. R. M Wolfert
Highly constrained multi-objective design and decision problems are difficult to solve because of strict feasibility requirements and conflicting stakeholder preferences. Evolution…
Adaptive parallelization of multi-agent simulations with localized dynamics
Alexandru-Ionuţ Băbeanu, Tatiana Filatova, Jan H. Kwakkel +1
Agent-based modelling constitutes a versatile approach to representing and simulating complex systems. Studying large-scale systems is challenging because of the computational time…
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
Stefano Teso, Laurens Bliek, Andrea Borghesi +4
It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variab…
Learning to branch with Tree MDPs
Lara Scavuzzo, Feng Yang Chen, Didier Chételat +4
State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule. The idea of learni…
Optimal training of integer-valued neural networks with mixed integer programming
Tómas Thorbjarnarson, Neil Yorke-Smith
Recent work has shown potential in using Mixed Integer Programming (MIP) solvers to optimize certain aspects of neural networks (NNs). However the intriguing approach of training N…
A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP
Kaan Yilmaz, Neil Yorke-Smith
In line with the growing trend of using machine learning to help solve combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer p…
Towards a Framework for Certification of Reliable Autonomous Systems
Michael Fisher, Viviana Mascardi, Kristin Yvonne Rozier +3
A computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control. The capability and spread of suc…
Order Acceptance and Scheduling with Sequence-dependent Setup Times: a New Memetic Algorithm and Benchmark of the State of the Art
Lei He, Arthur Guijt, Mathijs de Weerdt +2
The Order Acceptance and Scheduling (OAS) problem describes a class of real-world problems such as in smart manufacturing and satellite scheduling. This problem consists of simulta…
Uncertainty in Soft Temporal Constraint Problems:A General Framework and Controllability Algorithms for the Fuzzy Case
F. Rossi, K. B. Venable, N. Yorke-Smith
In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both pr…
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
Neil Yorke-Smith, Carmen Gervet
Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such pro…