All Outputs (8)

Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation (2016)
Journal Article

Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. Ho... Read More about Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation.

Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem (2016)
Journal Article

Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of loc... Read More about Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem.

A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings (2016)
Journal Article

Memetic algorithms are a class of well-studied metaheuristics which combine evolutionary algorithms and local search techniques. A meme represents contagious piece of information in an adaptive information sharing system. The canonical memetic algori... Read More about A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings.

CHAMP: Creating Heuristics via Many Parameters for online bin packing (2016)
Journal Article

The online bin packing problem is a well-known bin packing variant which requires immediate decisions to be made for the placement of a lengthy sequence of arriving items of various sizes one at a time into fixed capacity bins without any overflow. T... Read More about CHAMP: Creating Heuristics via Many Parameters for online bin packing.

A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem (2016)
Journal Article

© 2016 by the Massachusetts Institute of Technology. Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an exis... Read More about A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem.

A multi-agent based cooperative approach to scheduling and routing (2016)
Journal Article

In this study, we propose a general agent-based distributed framework where each agent is implementing a different metaheuristic/local search combination. Moreover, an agent continuously adapts itself during the search process using a direct cooperat... Read More about A multi-agent based cooperative approach to scheduling and routing.

Iterated local search using an add and delete hyper- heuristic for university course timetabling (2016)
Journal Article

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete... Read More about Iterated local search using an add and delete hyper- heuristic for university course timetabling.

A tensor based hyper-heuristic for nurse rostering (2016)
Journal Article

Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly prefe... Read More about A tensor based hyper-heuristic for nurse rostering.