School of Engineering and Information Technology


Fitness Landscape Analysis Using Network Motifs

A new predictive difficulty measure for evolutionary algorithms (EAs) using network motifs, namely Motif Difficulty (MD), is proposed in this research. A new kind of motifs, namely Distance Motifs, is designed and is divided into three classes based on the contribution to the search process of EAs. These classes of motifs are named as Guide Motifs, Deceptive Motifs, and Neutral Motifs. Finally, MD is designed by synthesizing the effect of these 3 classes of motifs. Additionally, since exhaustive computation on the whole networks gets quickly impractical, a sampling technique especially for computing approximate MD is proposed. We [Liu, Zhong, Green & Abbass] analyse the effect of two representations, namely binary and permutation, on the difficulty of multidimensional knapsack problems. The results also confirm previous measures; that is, permutation representation with a first-fit heuristic is better than binary representation.



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Other research projects for Computational Intelligence during 2011:

 A Computational Linguistic Approach for the Identification of Translator Stylometry in Arabic-English Text
 Adversarial Evolution
 Adversarial Learning
 Aircraft User-Preferred Routes using dynamic network of control points
 Ant Colony Optimization Algorithm for Controlling Swarm Robots
 Automation in Air Traffic Control
 Community Detection in Complex Networks
 Competency Awareness in Strategic Decision Making
 Cognition-centric assessment of risk in transportation systems
 Dynamic Airspace Sectorisation
 Environmental Impact of Aviation
 Evolving Strategic Stories
 Fleet Optimisation for Defence Logistics Using Evolutionary Rule-Based Ensembles
 Human Factors in Air Traffic Control
 Interdependent Security in Air and Land Transport
 Risk Assessment of Air Traffic Controllers Tools for Conflict Detection (MTCD & TCT)
 Safety web for Air Traffic Control