Adaptive Optimisation
All known stochastic optimisation methods such as Simulated Annealing, Evolutionary Algorithms and Estimation of Distribution Algorithms have a range of adjustable parameters like learning rates, crossover probabilities and weighting factors. Poor algorithm parameterisation hinders the discovery of good solutions.
In this project, we are investigating ways of adjusting and adapting algorithm parameters during the optimisation process, with the goal of achieving optimal algorithm performance. The main stages of the adaptive optimisation framework are shown in the figure below.
Download source code
- i) To run the project from the command line, go to the dist folder and type the following:
- java -jar "AdaptiveOptimisation.jar"
- ii) You may change the experimental settings in
properties.xml