Immunology as a Metaphor for Computational ... - Napier University

6.7 Performance of SOSDM in non-stationary environments . . . . . . . 1676.7.1 Update of data within fixed clusters . . . . . . . . . . . . . . 1686.7.2 Appearance of new clusters . . . . . . . . . . . . . . . . . . 1696.7.3 Making the System Truly Dynamic . . . . . . . . . . . . . . 1736.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1767 Conclusion 1797.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1797.2 Were the aims achieved ? . . . . . . . . . . . . . . . . . . . . . . . . 1797.2.1 Scheduling Models, SCHED1-IS and SCHED2-IS . . . . . . 1807.2.2 Data-Clustering Models, COSDM and SOSDM . . . . . . . . 1827.3 Is Immunology a Useful Metaphor ? . . . . . . . . . . . . . . . . . . 1867.3.1 Other approaches to scheduling . . . . . . . . . . . . . . . . 1867.3.2 Other Approaches to Data-Clustering . . . . . . . . . . . . . 1887.4 Suggestions for Future Work . . . . . . . . . . . . . . . . . . . . . . 191A Coincidences in permutations and schedules 193B Experimental results obtained using SOSDM 195Bibliography 202ix