Now showing items 1-10 of 17
Optimisation of a Stagger Chart for Aviation Fleet Planning
(Multidsciplinary International Scheduling Conference: Theory and Applications (MISTA 2015), 2015)
Hyper-learning for population-based incremental learning in dynamic environments.
The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. Recently, the PBIL algorithm has been applied for dynamic optimization problems. This paper ...
Automated control of an actively compensated Langmuir probe system using simulated annealing
Continuous Parameter Pools in Ensemble Differential Evolution
Ensemble of parameters and mutation strategies differential evolution (EPSDE) is an elegant promising optimization framework based on the idea that a pool of mutation and crossover strategies along, with associated pools ...
The Importance of Being Structured: a Comparative Study on Multi Stage Memetic Approaches
Memetic Computing (MC) is a discipline which studies optimization algorithms and sees them as structures of operators, the memes. Although the choice of memes is crucial for an effective algorithmic design, special attention ...
Meta-Lamarckian learning in three stage optimal memetic exploration
(IEEE Xplore, 2012-09)
Three Stage Optimal Memetic Exploration (3SOME) is a single-solution optimization algorithm where the coordinated action of three distinct operators progressively perturb the solution in order to progress towards the ...
A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms
(Springer Berlin Heidelberg, 2014-11)
The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the ...
Multicriteria adaptive differential evolution for global numerical optimization
(IOS Press, 2015-02-01)
Differential evolution (DE) has become a prevalent tool for global optimization problems since it was proposed in 1995. As usual, when applying DE to a specific problem, determining the most proper strategy and its associated ...
Structural bias in population-based algorithms
Abstract Challenging optimisation problems are abundant in all areas of science and industry. Since the 1950s, scientists have responded to this by developing ever-diversifying families of ‘black box’ optimisation algorithms. ...
A comparison of three Differential Evolution strategies in terms of early convergence with different population sizes
Differential Evolution (DE) is a popular population-based continuous optimization algorithm that generates new candidate solutions by perturbing the existing ones, using scaled differences of randomly selected solutions ...