Now showing items 1-10 of 17
Logan's run: Lane optimisation using genetic algorithms based on nsga-ii
Whilst bus lanes are an important tool to ensure bus time reliability their presence can be detrimental to urban traffic. In this paper a Non-dominated Sorting Genetic Algorithm (NSGA-II) has been adopted to study the ...
Automated control of an actively compensated Langmuir probe system using simulated annealing
Optimisation of a Stagger Chart for Aviation Fleet Planning
(Multidsciplinary International Scheduling Conference: Theory and Applications (MISTA 2015), 2015)
Structural Bias in Differential Evolution: a preliminary study
This paper extends the study of structural bias in popular metaheuristic global optimisation methods. Previously, it has been shown that both Genetic Algorithms and Particle Swarm Optimisation suffer from such bias. This ...
Robot Base Disturbance Optimization with Compact Differential Evolution Light
(Springer Berlin Heidelberg, 2012-04)
Despite the constant growth of the computational power in consumer electronics, very simple hardware is still used in space applications. In order to obtain the highest possible reliability, in space systems limited-power ...
Three variants of three Stage Optimal Memetic Exploration for handling non-separable fitness landscapes
Three Stage Optimal Memetic Exploration (3SOME) is a recently proposed algorithmic framework which sequentially perturbs a single solution by means of three operators. Although 3SOME proved to be extremely successful at ...
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 ...
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 ...
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 ...
Compact differential evolution light: high performance despite limited memory requirement and modest computational overhead
(Springer US, 2012-09-01)
Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithms ...