3,130 research outputs found
Memetic cooperative coevolution of Elman recurrent neural networks
Cooperative coevolution decomposes an optimi-
sation problem into subcomponents and collectively solves
them using evolutionary algorithms. Memetic algorithms
provides enhancement to evolutionary algorithms with local
search. Recently, the incorporation of local search into a
memetic cooperative coevolution method has shown to be
efficient for training feedforward networks on pattern classification problems. This paper applies the memetic cooperative coevolution method for training recurrent neural networks on grammatical inference problems. The results show
that the proposed method achieves better performance in
terms of optimisation time and robustness
The Impact of Coevolution and Abstention on the Emergence of Cooperation
This paper explores the Coevolutionary Optional Prisoner's Dilemma (COPD)
game, which is a simple model to coevolve game strategy and link weights of
agents playing the Optional Prisoner's Dilemma game. We consider a population
of agents placed in a lattice grid with boundary conditions. A number of Monte
Carlo simulations are performed to investigate the impacts of the COPD game on
the emergence of cooperation. Results show that the coevolutionary rules enable
cooperators to survive and even dominate, with the presence of abstainers in
the population playing a key role in the protection of cooperators against
exploitation from defectors. We observe that in adverse conditions such as when
the initial population of abstainers is too scarce/abundant, or when the
temptation to defect is very high, cooperation has no chance of emerging.
However, when the simple coevolutionary rules are applied, cooperators
flourish.Comment: To appear at Studies in Computational Intelligence (SCI), Springer,
201
Optimal interdependence between networks for the evolution of cooperation
Recent research has identified interactions between networks as crucial for the outcome of evolutionary
games taking place on them. While the consensus is that interdependence does promote cooperation by
means of organizational complexity and enhanced reciprocity that is out of reach on isolated networks, we
here address the question just how much interdependence there should be. Intuitively, one might assume
the more the better. However, we show that in fact only an intermediate density of sufficiently strong
interactions between networks warrants an optimal resolution of social dilemmas. This is due to an intricate
interplay between the heterogeneity that causes an asymmetric strategy flow because of the additional links
between the networks, and the independent formation of cooperative patterns on each individual network.
Presented results are robust to variations of the strategy updating rule, the topology of interdependent
networks, and the governing social dilemma, thus suggesting a high degree of universality
Coevolutionary games - a mini review
Prevalence of cooperation within groups of selfish individuals is puzzling in
that it contradicts with the basic premise of natural selection. Favoring
players with higher fitness, the latter is key for understanding the challenges
faced by cooperators when competing with defectors. Evolutionary game theory
provides a competent theoretical framework for addressing the subtleties of
cooperation in such situations, which are known as social dilemmas. Recent
advances point towards the fact that the evolution of strategies alone may be
insufficient to fully exploit the benefits offered by cooperative behavior.
Indeed, while spatial structure and heterogeneity, for example, have been
recognized as potent promoters of cooperation, coevolutionary rules can extend
the potentials of such entities further, and even more importantly, lead to the
understanding of their emergence. The introduction of coevolutionary rules to
evolutionary games implies, that besides the evolution of strategies, another
property may simultaneously be subject to evolution as well. Coevolutionary
rules may affect the interaction network, the reproduction capability of
players, their reputation, mobility or age. Here we review recent works on
evolutionary games incorporating coevolutionary rules, as well as give a
didactic description of potential pitfalls and misconceptions associated with
the subject. In addition, we briefly outline directions for future research
that we feel are promising, thereby particularly focusing on dynamical effects
of coevolutionary rules on the evolution of cooperation, which are still widely
open to research and thus hold promise of exciting new discoveries.Comment: 24 two-column pages, 10 figures; accepted for publication in
BioSystem
Sustainable Cooperative Coevolution with a Multi-Armed Bandit
This paper proposes a self-adaptation mechanism to manage the resources
allocated to the different species comprising a cooperative coevolutionary
algorithm. The proposed approach relies on a dynamic extension to the
well-known multi-armed bandit framework. At each iteration, the dynamic
multi-armed bandit makes a decision on which species to evolve for a
generation, using the history of progress made by the different species to
guide the decisions. We show experimentally, on a benchmark and a real-world
problem, that evolving the different populations at different paces allows not
only to identify solutions more rapidly, but also improves the capacity of
cooperative coevolution to solve more complex problems.Comment: Accepted at GECCO 201
Towards real-world complexity: an introduction to multiplex networks
Many real-world complex systems are best modeled by multiplex networks of
interacting network layers. The multiplex network study is one of the newest
and hottest themes in the statistical physics of complex networks. Pioneering
studies have proven that the multiplexity has broad impact on the system's
structure and function. In this Colloquium paper, we present an organized
review of the growing body of current literature on multiplex networks by
categorizing existing studies broadly according to the type of layer coupling
in the problem. Major recent advances in the field are surveyed and some
outstanding open challenges and future perspectives will be proposed.Comment: 20 pages, 10 figure
Conformity enhances network reciprocity in evolutionary social dilemmas
The pursuit of highest payoffs in evolutionary social dilemmas is risky and
sometimes inferior to conformity. Choosing the most common strategy within the
interaction range is safer because it ensures that the payoff of an individual
will not be much lower than average. Herding instincts and crowd behavior in
humans and social animals also compel to conformity on their own right.
Motivated by these facts, we here study the impact of conformity on the
evolution of cooperation in social dilemmas. We show that an appropriate
fraction of conformists within the population introduces an effective surface
tension around cooperative clusters and ensures smooth interfaces between
different strategy domains. Payoff-driven players brake the symmetry in favor
of cooperation and enable an expansion of clusters past the boundaries imposed
by traditional network reciprocity. This mechanism works even under the most
testing conditions, and it is robust against variations of the interaction
network as long as degree-normalized payoffs are applied. Conformity may thus
be beneficial for the resolution of social dilemmas.Comment: 8 two-column pages, 5 figures; accepted for publication in Journal of
the Royal Society Interfac
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