more biological inspiration for evolutionary algorithms

The fact is that the bulk of EA techniques relies on a very simplified model of biological genetics, using haploid crossover and point mutation. While a few researchers have investigated diploid crossover:

– Smith, R. E. (1988) An investigation of diploid genetic algorithms for adaptive search of nonstationary functions. TCGA Report No. 88001, University of Alabama, The Clearinghouse for Genetic Algorithms, Tuscaloosa (Smith 1988),

there is still a minimal application of the current knowledge of biological genetic processes. In particular, the understanding fo how genes regulate each other’s activity needs significant study (for example this?) with respect to artificial evolution.

Another fruitful area would be a comparative study of the transcription and translation mechanisms in an artificial evolution context. Similarly, the role that introns play in preserving useful schema is an active research domain (although this has been the subject of greater study in the genetic programming community, where junk genes lead to bloat of the tree representation genomes)



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