School of Engineering & IT
Evolutionary algorithms through adaptive decomposition
Evolutionary algorithms are implicitly parallel search techniques inspired by evolutionary principles.
Evolutionary algorithms are implicitly parallel search techniques inspired by evolutionary principles.
Program Code: 1885
Objectives:
Evolutionary algorithms are implicitly parallel search techniques inspired by evolutionary principles. Work in evolutionary algorithms can take many forms including biologically motivated models and statistical modelling of an optimization problem. The objective of this project is to develop decomposition techniques for evolutionary algorithms in optimization and/or machine learning problems. The decomposition needs to be self-organizing, as such, it is not a fixed predefined scheme of decomposition.
Expected Background Knowledge:
Knowledge or demonstrated ability to do programming in parallel highperformance computing environment using C or JAVA
Good understanding of Optimisation theory
Description of work:
A literature review of evolutionary algorithms for optimization and/or machine learning problems
A literature review of decomposition techniques
Creating a new evolutionary algorithm technique that is based on selforganized decomposition principles
Testing the new technique
Contact:
Prof Hussein Abbass h.abbass@adfa.edu.au
School of Engineering & IT