A Model Based on Biological Invasions for Island Evolutionary Algorithms

Migration strategy plays an important role in designing effective distributed evolutionary algorithms. Here, a novel migration model inspired to the phenomenon known as biological invasion is adopted. The migration strategy is implemented through a multistage process involving large invading subpopulations and their competition with native individuals. In this work such a general approach is used within an island parallel model adopting Differential Evolution as the local algorithm. The resulting distributed algorithm is evaluated on a set of well known test functions and its effectiveness is compared against that of a classical distributed Differential Evolution.

[1]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[2]  A. F. Ioffe,et al.  NEW MIGRATION SCHEME FOR PARALLEL DIFFERENTIAL EVOLUTION , 2006 .

[3]  Erick Cantú-Paz,et al.  A Summary of Research on Parallel Genetic Algorithms , 1995 .

[4]  Erick Cantú-Paz,et al.  Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms , 2001, J. Heuristics.

[5]  Zbigniew Skolicki,et al.  The influence of migration sizes and intervals on island models , 2005, GECCO '05.

[6]  Ivanoe De Falco,et al.  Satellite Image Registration by Distributed Differential Evolution , 2007, EvoWorkshops.

[7]  Rainer Storn,et al.  Differential Evolution-A simple evolution strategy for fast optimization , 1997 .

[8]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[9]  C. Kolar,et al.  Progress in invasion biology: predicting invaders. , 2001, Trends in ecology & evolution.

[10]  G. Leguizamon,et al.  Island Based Distributed Differential Evolution: An Experimental Study on Hybrid Testbeds , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[11]  A. Suarez,et al.  The evolutionary consequences of biological invasions , 2008, Molecular ecology.

[12]  N. Shigesada,et al.  Biological Invasions: Theory and Practice , 1997 .

[13]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[14]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

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

[16]  T. Blackburn,et al.  The role of propagule pressure in explaining species invasions. , 2005, Trends in ecology & evolution.

[17]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[18]  M. Pace,et al.  Understanding the long-term effects of species invasions. , 2006, Trends in ecology & evolution.

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  Ville Tirronen,et al.  Scale factor inheritance mechanism in distributed differential evolution , 2009, Soft Comput..

[21]  D. Simberloff.  The Role of Propagule Pressure in Biological Invasions , 2009 .

[22]  C. Nilsson,et al.  Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework , 2009 .

[23]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[24]  Ville Tirronen,et al.  Distributed differential evolution with explorative–exploitative population families , 2009, Genetic Programming and Evolvable Machines.

[25]  Marco Tomassini,et al.  Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series) , 2005 .

[26]  Heinz Mühlenbein,et al.  Evolution in Time and Space - The Parallel Genetic Algorithm , 1990, FOGA.