The Self-Organization of Interaction Networks for Nature-Inspired Optimization

• Published in: IEEE transactions on evolutionary computation Vol. 12; no. 2; pp. 220 - 230
• Main Authors: Whitacre, J.M., Sarker, R.A., Pham, Q.T.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-04-2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial intelligence; Australia; Behavior; Biological; Biological systems; Chemical engineering; Complex systems; Computer science; control theory; systems; Dynamical systems; Dynamics; Evolution (biology); Evolutionary algorithms; Evolutionary computation; Exact sciences and technology; Genetic algorithms; Genetics; Information technology; Lattices; Learning and adaptive systems; network evolution; Optimization; Optimization algorithms; Populations; Robustness; self-organization; Studies; sustainable diversity; Theoretical computing; self-organization; sustainable diversity; optimization; Complex systems; network evolution; evolutionary algorithms; Biological properties; Locality; Evolutionary algorithm; Complex system; Epistasis; Network management; Dynamical system; Gene expression; Cellular algorithm; Optimization; Biological system; Self organization; Biomimetics; Genetic algorithm; Robustness
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2007.900327

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.