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A Case for Codons in Evolutionary Algorithms
Joshua Gilbert and Maggie Eppstein
Dept. of Computer Science,
University of Vermont,
Burlington VT, 05405 USA.
{jgilbert,eppstein}@emba.uvm.edu
Abstract. A new method is developed for representation and encoding in population-based evolutionary algorithms. The method is inspired by the biological genetic code and utilizes a many-to-one, codon-based, genotype-to-phenotype translation scheme. A genetic algorithm was implemented with this codon-based representation using three different codon translation tables, each with different characteristics. A standard genetic algorithm is compared to the codon-based genetic algorithms on two difficult search problems; a dynamic knapsack problem and a static problem involving many suboptima. Results on these two problems indicate that the codon-based representation may promote rapid adaptation to changing environments and the ability to find global minima in highly non-convex problems.
LNCS 2723, p. 967 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003