Diversity and Multimodal Search with a Hybrid Two-Population GA: An Application to ANN Development

Being based on the theory of evolution and natural selection, the Genetic Algorithms (GA) represent a technique that has been proved as good enough for the resolution of those problems that require a search through a complex space of possible solutions. The maintenance of a population of possible solutions that are in constant evolution may lead to its diversity being lost, consequently it would be more difficult, not only the achievement of a final solution but also the supply of more than one solution The method that is described here tries to overcome those difficulties by means of a modification in traditional GA's. Such modification involves the inclusion of an additional population that might avoid the mentioned loss of diversity of classical GA's. This new population would also provide the piece of exhaustive search that allows to provide more than one solution.

[1]  David E. Goldberg,et al.  Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement , 1999 .

[2]  C. Darwin The Origin of Species by Means of Natural Selection, Or, The Preservation of Favoured Races in the Struggle for Life , 1859 .

[3]  Rasmus K. Ursem,et al.  Diversity-Guided Evolutionary Algorithms , 2002, PPSN.

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

[5]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[6]  Juan R. Rabuñal,et al.  Hybrid Two-Population Genetic Algorithm , 2001, Fuzzy Days.

[7]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[8]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[9]  Isao Ono,et al.  A Real Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distributed Crossover , 1997, ICGA.

[10]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[11]  Pedro J. Ballester,et al.  Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization , 2003, GECCO.

[12]  N. Garc'ia-Pedrajas,et al.  CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features , 2005, J. Artif. Intell. Res..

[13]  Albert Donally Bethke,et al.  Genetic Algorithms as Function Optimizers , 1980 .

[14]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[15]  Kalyanmoy Deb,et al.  A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization , 2002, Evolutionary Computation.