What can we learn from slow self-avoiding adaptive walks by an infinite radius search algorithm?

Details What can we learn from slow self-avoiding adaptive walks by an infinite radius search algorithm? What can we learn from slow self-avoiding adaptive walks by an infinite radius search algorithm?

2011-07-18

arxiv.org/abs/1107.3498v1

Computer Science

Neural and Evolutionary Computing

Scientific paper

Slow self-avoiding adaptive walks by an infinite radius search algorithm (Limax) are analyzed as themselves, and as the network they form. The study is conducted on several NK problems and two HIFF problems. We find that examination of such "slacker" walks and networks can indicate relative search difficulty within a family of problems, help identify potential local optima, and detect presence of structure in fitness landscapes. Hierarchical walks are used to differentiate rugged landscapes which are hierarchical (e.g. HIFF) from those which are anarchic (e.g. NK). The notion of node viscidity as a measure of local optimum potential is introduced and found quite successful although more work needs to be done to improve its accuracy on problems with larger K.

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