404 research outputs found
Supercooperation in Evolutionary Games on Correlated Weighted Networks
In this work we study the behavior of classical two-person, two-strategies
evolutionary games on a class of weighted networks derived from
Barab\'asi-Albert and random scale-free unweighted graphs. Using customary
imitative dynamics, our numerical simulation results show that the presence of
link weights that are correlated in a particular manner with the degree of the
link endpoints, leads to unprecedented levels of cooperation in the whole
games' phase space, well above those found for the corresponding unweighted
complex networks. We provide intuitive explanations for this favorable behavior
by transforming the weighted networks into unweighted ones with particular
topological properties. The resulting structures help to understand why
cooperation can thrive and also give ideas as to how such supercooperative
networks might be built.Comment: 21 page
Clustering of Local Optima in Combinatorial Fitness Landscapes
Using the recently proposed model of combinatorial landscapes: local optima
networks, we study the distribution of local optima in two classes of instances
of the quadratic assignment problem. Our results indicate that the two problem
instance classes give rise to very different configuration spaces. For the
so-called real-like class, the optima networks possess a clear modular
structure, while the networks belonging to the class of random uniform
instances are less well partitionable into clusters. We briefly discuss the
consequences of the findings for heuristically searching the corresponding
problem spaces.Comment: Learning and Intelligent OptimizatioN Conference (LION 5), Rome :
Italy (2011
Dynamics of Unperturbed and Noisy Generalized Boolean Networks
For years, we have been building models of gene regulatory networks, where
recent advances in molecular biology shed some light on new structural and
dynamical properties of such highly complex systems. In this work, we propose a
novel timing of updates in Random and Scale-Free Boolean Networks, inspired by
recent findings in molecular biology. This update sequence is neither fully
synchronous nor asynchronous, but rather takes into account the sequence in
which genes affect each other. We have used both Kauffman's original model and
Aldana's extension, which takes into account the structural properties about
known parts of actual GRNs, where the degree distribution is right-skewed and
long-tailed. The computer simulations of the dynamics of the new model compare
favorably to the original ones and show biologically plausible results both in
terms of attractors number and length. We have complemented this study with a
complete analysis of our systems' stability under transient perturbations,
which is one of biological networks defining attribute. Results are
encouraging, as our model shows comparable and usually even better behavior
than preceding ones without loosing Boolean networks attractive simplicity.Comment: 29 pages, publishe
Ensemble Learning for Free with Evolutionary Algorithms ?
Evolutionary Learning proceeds by evolving a population of classifiers, from
which it generally returns (with some notable exceptions) the single
best-of-run classifier as final result. In the meanwhile, Ensemble Learning,
one of the most efficient approaches in supervised Machine Learning for the
last decade, proceeds by building a population of diverse classifiers. Ensemble
Learning with Evolutionary Computation thus receives increasing attention. The
Evolutionary Ensemble Learning (EEL) approach presented in this paper features
two contributions. First, a new fitness function, inspired by co-evolution and
enforcing the classifier diversity, is presented. Further, a new selection
criterion based on the classification margin is proposed. This criterion is
used to extract the classifier ensemble from the final population only
(Off-line) or incrementally along evolution (On-line). Experiments on a set of
benchmark problems show that Off-line outperforms single-hypothesis
evolutionary learning and state-of-art Boosting and generates smaller
classifier ensembles
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
Conformity Hinders the Evolution of Cooperation on Scale-Free Networks
We study the effects of conformity, the tendency of humans to imitate locally
common behaviors, in the evolution of cooperation when individuals occupy the
vertices of a graph and engage in the one-shot Prisoner's Dilemma or the
Snowdrift game with their neighbors. Two different graphs are studied: rings
(one-dimensional lattices with cyclic boundary conditions) and scale-free
networks of the Barabasi-Albert type. The proposed evolutionary-graph model is
studied both by means of Monte Carlo simulations and an extended
pair-approximation technique. We find improved levels of cooperation when
evolution is carried on rings and individuals imitate according to both the
traditional pay-off bias and a conformist bias. More important, we show that
scale-free networks are no longer powerful amplifiers of cooperation when fair
amounts of conformity are introduced in the imitation rules of the players.
Such weakening of the cooperation-promoting abilities of scale-free networks is
the result of a less biased flow of information in scale-free topologies,
making hubs more susceptible of being influenced by less-connected neighbors.Comment: 14 pages, 11 figure
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