6,460 research outputs found
Fitness landscape of the cellular automata majority problem: View from the Olympus
In this paper we study cellular automata (CAs) that perform the computational
Majority task. This task is a good example of what the phenomenon of emergence
in complex systems is. We take an interest in the reasons that make this
particular fitness landscape a difficult one. The first goal is to study the
landscape as such, and thus it is ideally independent from the actual
heuristics used to search the space. However, a second goal is to understand
the features a good search technique for this particular problem space should
possess. We statistically quantify in various ways the degree of difficulty of
searching this landscape. Due to neutrality, investigations based on sampling
techniques on the whole landscape are difficult to conduct. So, we go exploring
the landscape from the top. Although it has been proved that no CA can perform
the task perfectly, several efficient CAs for this task have been found.
Exploiting similarities between these CAs and symmetries in the landscape, we
define the Olympus landscape which is regarded as the ''heavenly home'' of the
best local optima known (blok). Then we measure several properties of this
subspace. Although it is easier to find relevant CAs in this subspace than in
the overall landscape, there are structural reasons that prevent a searcher
from finding overfitted CAs in the Olympus. Finally, we study dynamics and
performance of genetic algorithms on the Olympus in order to confirm our
analysis and to find efficient CAs for the Majority problem with low
computational cost
Scuba Search : when selection meets innovation
We proposed a new search heuristic using the scuba diving metaphor. This
approach is based on the concept of evolvability and tends to exploit
neutrality in fitness landscape. Despite the fact that natural evolution does
not directly select for evolvability, the basic idea behind the scuba search
heuristic is to explicitly push the evolvability to increase. The search
process switches between two phases: Conquest-of-the-Waters and
Invasion-of-the-Land. A comparative study of the new algorithm and standard
local search heuristics on the NKq-landscapes has shown advantage and limit of
the scuba search. To enlighten qualitative differences between neutral search
processes, the space is changed into a connected graph to visualize the
pathways that the search is likely to follow
A Study of NK Landscapes' Basins and Local Optima Networks
We propose a network characterization of combinatorial fitness landscapes by
adapting the notion of inherent networks proposed for energy surfaces (Doye,
2002). We use the well-known family of landscapes as an example. In our
case the inherent network is the graph where the vertices are all the local
maxima and edges mean basin adjacency between two maxima. We exhaustively
extract such networks on representative small NK landscape instances, and show
that they are 'small-worlds'. However, the maxima graphs are not random, since
their clustering coefficients are much larger than those of corresponding
random graphs. Furthermore, the degree distributions are close to exponential
instead of Poissonian. We also describe the nature of the basins of attraction
and their relationship with the local maxima network.Comment: best paper nominatio
Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms
A significant challenge in nature-inspired algorithmics is the identification
of specific characteristics of problems that make them harder (or easier) to
solve using specific methods. The hope is that, by identifying these
characteristics, we may more easily predict which algorithms are best-suited to
problems sharing certain features. Here, we approach this problem using fitness
landscape analysis. Techniques already exist for measuring the "difficulty" of
specific landscapes, but these are often designed solely with evolutionary
algorithms in mind, and are generally specific to discrete optimisation. In
this paper we develop an approach for comparing a wide range of continuous
optimisation algorithms. Using a fitness landscape generation technique, we
compare six different nature-inspired algorithms and identify which methods
perform best on landscapes exhibiting specific features.Comment: 10 pages, 1 figure, submitted to the 11th International Conference on
Adaptive and Natural Computing Algorithm
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