Abstract
We describe how the standard genotype-phenotype mapping process of Grammatical Evolution (GE) can be enhanced with an attribute grammar to allow GE to operate as a decoder-based Evolutionary Algorithm (EA). Use of an attribute grammar allows GE to maintain context-sensitive and semantic information pertinent to the capacity constraints of the 01 Multiconstrained Knapsack Problem (MKP). An attribute grammar specification is used to perform decoding similar to a first-fit heuristic. The results presented are encouraging, demonstrating that GE in conjunction with attribute grammars can provide an improvement over the standard context-free mapping process for problems in this domain.
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Cleary, R., O’Neill, M. (2005). An Attribute Grammar Decoder for the 01 MultiConstrained Knapsack Problem. In: Raidl, G.R., Gottlieb, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31996-2_4
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DOI: https://doi.org/10.1007/978-3-540-31996-2_4
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