Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Practical Handbook of Genetic Algorithms: New Frontiers
Practical Handbook of Genetic Algorithms: New Frontiers
The Darwinian Genetic Code: An Adaptation for Adapting?
Genetic Programming and Evolvable Machines
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic programming using genotype-phenotype mapping from linear genomes into linear phenotypes
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
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.