TY  - GEN
ID  - cogprints6578
UR  - http://cogprints.org/6578/
A1  - Whitacre, Dr James M
A1  - Sarker, Dr Ruhul A
A1  - Pham, Dr Q. Tuan
Y1  - 2008///
N2  - 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 or in Evolutionary Algorithms 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.
KW  - Complex Systems
KW  -  Evolutionary Algorithms
KW  -  Network Evolution
KW  -  Optimization
KW  -  Self-Organization
KW  -  Sustainable Diversity
TI  - The Self-Organization of Interaction Networks for Nature-Inspired Optimization
SP  - 220
AV  - public
EP  - 230
ER  -

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