Abstract
Previous publications by the authors have demonstrated a bimodal performance profile for simple evolutionary search on variants of the Adaptive Distributed Database Management Problem (ADDMP) and other problems over a range of evaluation limits. This paper examines an anomaly seen in one of these profiles and together with results from a range of other problems, shows that with sufficiently high evaluation limits, a multimodal performance profile is apparent in search spaces with significant numbers of deceptive local optima. This is particularly apparent in the performance profile of the Hierarchial If and only If problem (H-IFF) where the regular structure of the search space produces several distinct peaks and troughs in the performance profile, possibly indicative of a range of specific ‘fitness barriers’ which are surmountable by specific rates of mutation. This observation could prove important in general EA parameter tuning over a range of problems with similar characteristics. Further, the existence of optimal mutation rates inducing a minimum in standard deviation of run-time, is of critical importance in the application of EAs to realtime, real-world problems.
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References
T Bäck, Evolutionary Algorithms in Theory and Practice, Oxford University Press, 1996
K Deb and S Agrawal: Understanding Interactions among Genetic Algorithm Parameters. in Foundations of Genetic Algorithms 1998, Morgan Kaufmann.
D Goldberg (1989), Genetic Algorithms in Search Optimisation and Machine Learning, Addison Wesley.
J Holland, Adaptation in Natural and Artificial Systems, MIT press, Cambridge, MA, 1993
Kauffman, S.A., The Origings of Order: Self-Organization and Selection in Evolution, Oxford University Press, 1993
Z Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, 1996.
H Mühlenbein and D Schlierkamp-Voosen (1994), The Science of Breeding and its application to the Breeder Genetic Algorithm, Evolutionary Computation 1, pp. 335–360.
H Mühlenbein, How genetic algorithms really work: I. Mutation and hillclimbing, in R. Manner, B. Manderick (eds), Proc. of 2nd Intl Conference on Parallel Problem Solving from Nature, Elsevier, pp 15–25.
E van Nimwegen and J Crutchfield: Optmizing Epochal Evolutionary Search: Population-Size Independent Theory, Santa Fe Institute Working Paper 98-06-046, also submitted to Computer Methods in Applied Mechanics and Engineering, special issue on Evolutionary and Genetic Algorithms in Computational Mechanics and Engineering, D Goldberg and K Deb, editors, 1998.
E van Nimwegen and J Crutchfield: Optmizing Epochal Evolutionary Search: Population-Size Dependent Theory, Santa Fe Institute Working Paper 98-10-090, also submitted to Machine Learning, 1998.
M Oates, D Corne and R Loader, Investigating Evolutionary Approaches for Self-Adaption in Large Distributed Databases, in Proceedings of the 1998 IEEE ICEC, pp. 452–457.
M Oates and D Corne, QoS based GA Parameter Selection for Autonomously Managed Distributed Information Systems, in Procs of ECAI 98, the 1998 European Conference on Artificial Intelligence, pp. 670–674.
M Oates and D Corne, Investigating Evolutionary Approaches to Adaptive Database Management against various Quality of Service Metrics, LNCS, Procs of 5th Intl Conf on Parallel Problem Solving from Nature, PPSN-V (1998), pp. 775–784.
M Oates, Autonomous Management of Distributed Information Systems using Evolutionary Computing Techniques, Computing Anticipatory Systems, AIP Conf Procs 465, 1998, pp. 269–281.
M Oates, D Corne and R Loader, Skewed Crossover and the Dynamic Distributed Database Problem, Artificial Neural Networks and Genetic Algorithms 1999, Dobnikar et al (eds), Springer pp 280–287.
M Oates, D Corne and R Loader, Investigation of a Characteristic Bimodal Convergencetime/Mutation-rate Feature in Evolutionary Search, in Procs of Congress on Evolutionary Computation 99 Vol 3, IEEE, pp. 2175–2182
Oates M, Corne D and Loader R, Variation in Evolutionary Algorithm Performance Characteristics on the Adaptive Distributed Database Management Problem, in Procs of Genetic and Evolutionary Computation Conference 99, Morgan Kaufmann, pp.480–487
M. Oates, J. Smedley, D. Corne, R. Loader, Bimodal Performance Profile of Evolutionary Search and the Effects of Crossover, in Procs of 1999 Evonet Summer School on Theoretical aspects of Evolutionary Computation.
G Syswerda (1989), Uniform Crossover in Genetic Algorithms, in Schaffer J. (ed), Procs of the Third Int. Conf. on Genetic Algorithms. Morgan Kaufmann, pp. 2–9
Watson RA, Hornby GS, and Pollack JB, Modelling Building-Block Interdependency, LNCS, Procs of 5th Intl Conf on Parallel Problem Solving from Nature, PPSN-V (1998), pp. 97–106.
Watson RA, Pollack JB, Hierarchically Consistent Test Problems for Genetic Algorithms, in Procs of Congress on Evolutionary Computation 99 Vol 2, IEEE, pp. 1406–1413
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Oates, M., Corne, D., Loader, R. (2000). Multimodal Performance Profiles on the Adaptive Distributed Database Management Problem. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_22
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DOI: https://doi.org/10.1007/3-540-45561-2_22
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