Dynamic Models of Partially Connected Topologies for Population-Based Metaheuristics

This paper investigates the emergent properties of a self-organized dynamic network for structured population-based metaheuristics. The system displays complex and emergent behavior whose most visible trait is the self-organization of the population into dynamic clusters. Furthermore, relevant variables that describe the system display $1/f$ noise, which is a characteristic of many complex systems. These properties were previously detected with a time-invariant population (i.e., individuals with fixed fitness vales). In this work, the investigation is extended to dynamic populations (time-varying fitness values), a scenario that models more accurately the behavior of population-based metaheuristics. Several types of fitness variation rules were tested. The experiments show that dynamic populations also display the self-organizing properties and the behavioral patterns of the stationary fitness version, as long as the intensity of the changes is kept below a certain level.

[1]  J. J. Merelo,et al.  Towards a 2-dimensional self-organized framework for structured population-based metaheuristics , 2012, 2012 IEEE International Conference on Complex Systems (ICCS).

[2]  EvolutionaryAlgorithms Erick Cant.  Migration Policies , Selection Pressure , and Parallel , 2007 .

[3]  Márk Jelasity,et al.  Peer-to-Peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms , 2009, EvoWorkshops.

[4]  Agostinho C. Rosa,et al.  Performance and Scalability of Particle Swarms with Dynamic and Partially Connected Grid Topologies , 2018, IJCCI.

[5]  Pascal Bouvry,et al.  Designing a Self-Organized Approach for Scheduling Bag-of-Tasks , 2012, 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[6]  P.-P. Grasse.  La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs , 1959, Insectes Sociaux.

[7]  Ruhul A. Sarker,et al.  The Self-Organization of Interaction Networks for Nature-Inspired Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[8]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Juan Julián Merelo Guervós,et al.  EvAg: a scalable peer-to-peer evolutionary algorithm , 2010, Genetic Programming and Evolvable Machines.

[10]  D. Fogel.  Evolutionary algorithms in theory and practice , 1997, Complex..

[11]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[12]  Klaus Wehrle,et al.  Peer-to-Peer Systems and Applications , 2005, Peer-to-Peer Systems and Applications.

[13]  Juan Julián Merelo Guervós,et al.  Resilience to churn of a peer-to-peer evolutionary algorithm , 2008, Int. J. High Perform. Syst. Archit..

[14]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[15]  Grant Dick,et al.  Spatially-Structured Evolutionary Algorithms and Sharing: Do They Mix? , 2006, SEAL.

[16]  Marco Tomassini,et al.  Takeover time curves in random and small-world structured populations , 2005, GECCO '05.

[17]  Enrique Alba,et al.  The exploration/exploitation tradeoff in dynamic cellular genetic algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[18]  Boleslaw K. Szymanski,et al.  An asynchronous hybrid genetic-simplex search for modeling the Milky Way galaxy using volunteer computing , 2008, GECCO '08.