Pair approximations of takeover dynamics in regular population structures
Evolutionary Computation
A swarm algorithm for a self-structured P2P information system
IEEE Transactions on Evolutionary Computation
Evolutionary dynamics on scale-free interaction networks
IEEE Transactions on Evolutionary Computation
EvAg: a scalable peer-to-peer evolutionary algorithm
Genetic Programming and Evolvable Machines
Scale-free fully informed particle swarm optimization algorithm
Information Sciences: an International Journal
Sexual recombination in self-organizing interaction networks
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
Multi-robot navigation based QoS routing in self-organizing networks
Engineering Applications of Artificial Intelligence
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Over the last decade, significant progress has been made in understanding complex biological systems, however, there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in evolutionary algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical evolutionary algorithm (EA) or in EAs with structured populations such as the cellular genetic algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system.