Abstract
This chapter reviews and expands our work on the relationship between linkage structure, that is how decision variables of a problem are linked with (dependent on) one another, and the performance of three basic types of genetic evolutionary algorithms (GEAs): hill climbing, genetic algorithm and bottom-up self-assembly (compositional). It explores how concepts and quantitative methods from the field of social/complex networks can be used to characterize or explain problem difficulty for GEAs. It also re-introduces two novel concepts – inter-level conflict and specificity – which view linkage structure from a level perspective. In general, the basic GEAs performed well on our test problems with linkage structures resembling those empirically observed in many real-world networks. This is a positive indication that the structure of real-world networks which evolved without any central organization such as biological networks is not only influenced by evolution and therefore exhibit non-random properties, but also influences its own evolution in the sense that certain structures are easier for evolutionary forces to adapt for survival. However, this necessarily implies the difficulty of certain other structures. Hence, the need to go beyond basic GEAs to what we call GEAs with “brains”, of which linkage-learning GEAs is one species.
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Khor, S. (2010). Linkage Structure and Genetic Evolutionary Algorithms. In: Chen, Yp. (eds) Exploitation of Linkage Learning in Evolutionary Algorithms. Evolutionary Learning and Optimization, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12834-9_1
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