- author = "Keith Bearpark",
- title = "Learning and memory in genetic programming",
- school = "School of Engineering Sciences, University of Southampton",
- year = "2000",
- address = "UK",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://eprints.soton.ac.uk/45930/",
-
URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327359",
-
abstract = "Genetic Programming is a form of Evolutionary
Computation in which computer programs are evolved by
methods based on simulating the natural evolution of
biological species. A new generation of a species
acquires the characteristics of previous generations
through the inheritance of genes by sexual reproduction
and through random changes in alleles by random
mutation. The new generation may enhance its ability to
survive by the acquisition of cultural knowledge
through learning processes.
This thesis combines the transfer of knowledge by genetic means with the transfer of knowledge by cultural means. In particular, it introduces a new evolutionary operator, memory operator. In conventional genetic programming systems, a new generation is formed from a mating pool whose members are selected from the fittest members of previous generation. The new generation is produced by the exchange of genes between members of the mating pool and the random replacement of genes in the offspring. The new generation may or may not be able to survive better than its predecessor in a given environment.
The memory operator augments the evolutionary process by inserting into new chromosomes genetic material known to often result in fitness improvements. This material is acquired through a learning process in which the system is required to evolve generations that survive in a less demanding environment. The cultural knowledge acquired in this learning process is applied as an intelligent form of mutation to aid survival in a more demanding environment.",
- notes = "uk.bl.ethos.327359",
