937 research outputs found
Using network mesures to test evolved NK-landscapes
In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances that maximize the estimation of distribution algorithm complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K
Local Optima Networks of NK Landscapes with Neutrality
In previous work we have introduced a network-based model that abstracts many
details of the underlying landscape and compresses the landscape information
into a weighted, oriented graph which we call the local optima network. The
vertices of this graph are the local optima of the given fitness landscape,
while the arcs are transition probabilities between local optima basins. Here
we extend this formalism to neutral fitness landscapes, which are common in
difficult combinatorial search spaces. By using two known neutral variants of
the NK family (i.e. NKp and NKq) in which the amount of neutrality can be tuned
by a parameter, we show that our new definitions of the optima networks and the
associated basins are consistent with the previous definitions for the
non-neutral case. Moreover, our empirical study and statistical analysis show
that the features of neutral landscapes interpolate smoothly between landscapes
with maximum neutrality and non-neutral ones. We found some unknown structural
differences between the two studied families of neutral landscapes. But
overall, the network features studied confirmed that neutrality, in landscapes
with percolating neutral networks, may enhance heuristic search. Our current
methodology requires the exhaustive enumeration of the underlying search space.
Therefore, sampling techniques should be developed before this analysis can
have practical implications. We argue, however, that the proposed model offers
a new perspective into the problem difficulty of combinatorial optimization
problems and may inspire the design of more effective search heuristics.Comment: IEEE Transactions on Evolutionary Computation volume 14, 6 (2010) to
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Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and Open Challenges
Research on new optimization algorithms is often funded based on the
motivation that such algorithms might improve the capabilities to deal with
real-world and industrially relevant optimization challenges. Besides a huge
variety of different evolutionary and metaheuristic optimization algorithms,
also a large number of test problems and benchmark suites have been developed
and used for comparative assessments of algorithms, in the context of global,
continuous, and black-box optimization. For many of the commonly used synthetic
benchmark problems or artificial fitness landscapes, there are however, no
methods available, to relate the resulting algorithm performance assessments to
technologically relevant real-world optimization problems, or vice versa. Also,
from a theoretical perspective, many of the commonly used benchmark problems
and approaches have little to no generalization value. Based on a mini-review
of publications with critical comments, advice, and new approaches, this
communication aims to give a constructive perspective on several open
challenges and prospective research directions related to systematic and
generalizable benchmarking for black-box optimization
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