Adaptive and Multilevel Metaheuristics by Konstantin Chakhlevitch, Peter Cowling (auth.), Carlos

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

One of the keystones in sensible 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. shifting part of this customization attempt to the set of rules itself -endowing it with clever mechanisms to self-adapt to the matter- has been a protracted pursued target within the box of metaheuristics.

These mechanisms can contain diversified facets of the set of rules, resembling for instance, self-adjusting the parameters, self-adapting the functioning of inner parts, evolving seek recommendations, etc.

Recently, the belief of hyperheuristics, i.e., utilizing a metaheuristic layer for adapting the hunt by means of selectively utilizing various low-level heuristics, has additionally been becoming more popular. This quantity offers contemporary advances within the zone of adaptativeness in metaheuristic optimization, together with updated studies 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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In indirect GAs, a portion of problem-specific information is often injected into the chromosome. • For many hyperheuristics, a significant amount of parameter tuning is required in order to find good parameter settings for a given problem. • A large number of problem instances may be required for training and testing of the method in order to accumulate enough knowledge to make the right choice of low level heuristics. However, for many real-world problems the problem data are not easily available and randomly generated instances may not adequately represent the real distribution.

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