By Konstantin Chakhlevitch, Peter Cowling (auth.), Carlos Cotta, Marc Sevaux, Kenneth Sörensen (eds.)

One of the keystones in functional metaheuristic problem-solving is the truth that tuning the optimization strategy to the matter into account is important for reaching best functionality. This tuning/customization is generally within the palms of the set of rules fashion designer, and regardless of a few methodological makes an attempt, it mostly is still a systematic paintings. shifting part of this customization attempt to the set of rules itself -endowing it with shrewdpermanent mechanisms to self-adapt to the matter- has been an extended pursued aim within the box of metaheuristics.

These mechanisms can contain diverse elements of the set of rules, comparable to for instance, self-adjusting the parameters, self-adapting the functioning of inner elements, evolving seek options, etc.

Recently, the assumption of hyperheuristics, i.e., utilizing a metaheuristic layer for adapting the quest via selectively utilizing assorted low-level heuristics, has additionally been rising in popularity. This quantity provides fresh advances within the zone of adaptativeness in metaheuristic optimization, together with updated reports of hyperheuristics and self-adaptation in evolutionary algorithms, in addition to innovative works on adaptive, self-adaptive and multilevel metaheuristics, with program to either combinatorial and non-stop optimization.

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Adaptive and Multilevel Metaheuristics

One of many keystones in useful metaheuristic problem-solving is the truth that tuning the optimization strategy to the matter into account is important for reaching best functionality. This tuning/customization is generally within the palms of the set of rules dressmaker, and regardless of a few methodological makes an attempt, it principally is still a systematic artwork.

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Springer, Heidelberg (2003) 60. : Hyper-heuristics: learning to combine simple heuristics in bin-packing problems. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 942–948. Morgan Kaufmann, San Francisco (2002) 61. : Development and application of hyperheuristics to personnel scheduling. PhD Thesis, Department of Computer Science, University of Nottingham, UK (2003) 62. : Problem and heuristic search space strategies for job shop scheduling. ORSA Journal on Computing 7, 453–467 (1995) 63.

Scheduling nurses using a tabu-search hyperheuristic. , Petrovic, S. ) Proceedings of the 1st Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA 2003), Nottingham, UK, pp. 197–218 (2003) 16. : A hyperheuristic methodology for real-world scheduling. PhD Thesis, Department of Computing, University of Bradford, UK (2006) 17. : Choosing the Fittest Subset of Low Level Heuristics in a Hyperheuristic Framework. , Gottlieb, J. ) EvoCOP 2005. LNCS, vol. 3448, pp.

There are also theoretical arguments that any quest for generally good EA, thus generally good parameter settings, is lost a priori, for example the No Free Lunch theorem [84]. As hinted above, there is a perhaps more fundamental drawback of the parameter tuning approach. Recall how we defined it: finding good values for the parameters before the run of the algorithm and then running the algorithm using these values, which remain fixed during the run. However, a run of an EA is an intrinsically dynamic, adaptive process.

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