20,099 research outputs found
Evidence of coevolution in multi-objective evolutionary algorithms
This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of evolution
Open problems in artificial life
This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated
Link Prediction Based on Subgraph Evolution in Dynamic Social Networks
We propose a new method for characterizing the dynamics of complex networks with its application to the link prediction problem. Our approach is based on the discovery of network subgraphs (in this study: triads of nodes) and measuring their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads found in the network, then we show how it can help to discover and quantify the dynamic patterns of network evolution. We also propose the application of TTM to link prediction with an algorithm (called TTM-predictor) which shows good performance, especially for sparse networks analyzed in short time scales. The future applications and research directions of our approach are also proposed and discussed
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Analyzing Social Network Structures in the Iterated Prisoner's Dilemma with Choice and Refusal
The Iterated Prisoner's Dilemma with Choice and Refusal (IPD/CR) is an
extension of the Iterated Prisoner's Dilemma with evolution that allows players
to choose and to refuse their game partners. From individual behaviors,
behavioral population structures emerge. In this report, we examine one
particular IPD/CR environment and document the social network methods used to
identify population behaviors found within this complex adaptive system. In
contrast to the standard homogeneous population of nice cooperators, we have
also found metastable populations of mixed strategies within this environment.
In particular, the social networks of interesting populations and their
evolution are examined.Comment: 37 pages, uuencoded gzip'd Postscript (1.1Mb when gunzip'd) also
available via WWW at http://www.cs.wisc.edu/~smucker/ipd-cr/ipd-cr.htm
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