Summary
In this chapter we concentrate on one particular class of Global-Local Search Hybrids, Memetic Algorithms (MAs), and we describe the implementation of “self-assembling” mechanisms to produce the local searches the MA uses. To understand the context in which self-assembling is applied we discuss some important aspects of Memetic theory and how these concepts could be harnessed to implement more competitive MAs. Our implementation is tested in two problems, Maximum Contact Map Overlap Problem (MAX-CMO) and the NK-Landscape Problems.
Three lessons can be drawn from this paper:
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Memetic theory provides a rich set of metaphors and insights that can be harnessed within optimisation algorithms as to provide better search methods.
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The optimization of solutions can be done simultaneously with the self-assembling of local search strategies which can then be exploited by the Memetic Algorithm (or other metaheuristic)
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Local search strategies that are evolved to supply building blocks can greatly improve the quality of the search obtained by the Memetic Algorithm and do not seem to suffer from premature convergence (an ubiquitous problem for global-local hybrids).
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Krasnogor, N., Gustafson, S. (2005). Self-Assembling of Local Searchers in Memetic Algorithms. In: Hart, W.E., Smith, J.E., Krasnogor, N. (eds) Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32363-5_11
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