By Konstantin Chakhlevitch, Peter Cowling (auth.), Carlos Cotta, Marc Sevaux, Kenneth Sörensen (eds.)
One of the keystones in useful metaheuristic problem-solving is the truth that tuning the optimization strategy to the matter into consideration is essential for attaining best functionality. This tuning/customization is generally within the fingers of the set of rules dressmaker, and regardless of a few methodological makes an attempt, it mostly continues to be a systematic paintings. moving part of this customization attempt to the set of rules itself -endowing it with shrewdpermanent mechanisms to self-adapt to the matter- has been a protracted pursued objective within the box of metaheuristics.
These mechanisms can contain diversified points of the set of rules, reminiscent of for instance, self-adjusting the parameters, self-adapting the functioning of inner parts, evolving seek concepts, etc.
Recently, the belief of hyperheuristics, i.e., utilizing a metaheuristic layer for adapting the quest by means of selectively utilizing varied low-level heuristics, has additionally been rising in popularity. This quantity offers fresh advances within the quarter of adaptativeness in metaheuristic optimization, together with updated experiences of hyperheuristics and self-adaptation in evolutionary algorithms, in addition to leading edge works on adaptive, self-adaptive and multilevel metaheuristics, with program to either combinatorial and non-stop optimization.
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Extra resources for Adaptive and Multilevel Metaheuristics
Hyperheuristics for managing a large collection of low level heuristics to schedule personnel. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation (CEC 2003), pp. 1214–1221. IEEE Press, Los Alamitos (2003) 19. : Using a large set of low level heuristics in a hyperheuristic approach to personnel scheduling. I. ) Evolutionary Scheduling. Springer, Heidelberg (to appear, 2007) 20. : An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In: Proceedings of 2002 Congress on Evolutionary Computation (CEC 2002), pp.
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In fact, theoretical and experimental results can neatly complement each other in this area if for a (simple) objective function f : Rn → R theoretically optimal mutation step sizes can be calculated. Note that the problem and the algorithm must be simple to make the system tractable, since for a complex problem and/or algorithm a theoretical analysis is infeasible. , progress rate during a run. If experimentally obtained data show a good match with the theoretically derived values, then we can conclude that self-adaptation works in the sense that it is able to ﬁnd the near-optimal step sizes.
Adaptive and Multilevel Metaheuristics by Konstantin Chakhlevitch, Peter Cowling (auth.), Carlos Cotta, Marc Sevaux, Kenneth Sörensen (eds.)