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Short term memory in genetic programming

Short term memory in genetic programming
Short term memory in genetic programming
The recognition of useful informaiton, its retention in memory, and subsequent use plays an important part in the behaviour of many biological species. Information gained by experience in one generation can be propagated to subsequent generations by some form of teaching. Each generation can then supplement its taught learning by its own experience. In this paper we explore the role of memorized information in the performance of a Genetic Programming (GP) system that uses a tree structure as its representation. Memory is implemented in the form of a set of subtrees derived from successful members of each generation. The memory is used by a genetic operator similar to the mutation operator but with the following difference. In a tree-structured system the mutation operator replaces randomly selected sub-trees by new randomly-generated sub-trees. The memory operator replaces randomly selected sub-trees by sub-trees randomly randomly selected from the memory. To study the memory operator's impact a GP system is used to evolve a well-known expression from classical kinetics using fitness-based selction. The memory operator is used together with the common crossover and mutation operators. It is shown that the addition of a memory operator increases the probability of a successful evolution for this particular problem. At this stage we make no claim for its impact on other problems that have been successfully addressed by Genetic Programming.
9781852333003
309-320
Springer Verlag
Bearpark, K.
cf87d373-6c01-48e2-8070-0a6199ce2cd2
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Parmee, I.C.
Bearpark, K.
cf87d373-6c01-48e2-8070-0a6199ce2cd2
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Parmee, I.C.

Bearpark, K. and Keane, A.J. (2000) Short term memory in genetic programming. Parmee, I.C. (ed.) In Evolutionary Design and Manufacture: Selected Papers from ACDM '00. Springer Verlag. pp. 309-320 .

Record type: Conference or Workshop Item (Paper)

Abstract

The recognition of useful informaiton, its retention in memory, and subsequent use plays an important part in the behaviour of many biological species. Information gained by experience in one generation can be propagated to subsequent generations by some form of teaching. Each generation can then supplement its taught learning by its own experience. In this paper we explore the role of memorized information in the performance of a Genetic Programming (GP) system that uses a tree structure as its representation. Memory is implemented in the form of a set of subtrees derived from successful members of each generation. The memory is used by a genetic operator similar to the mutation operator but with the following difference. In a tree-structured system the mutation operator replaces randomly selected sub-trees by new randomly-generated sub-trees. The memory operator replaces randomly selected sub-trees by sub-trees randomly randomly selected from the memory. To study the memory operator's impact a GP system is used to evolve a well-known expression from classical kinetics using fitness-based selction. The memory operator is used together with the common crossover and mutation operators. It is shown that the addition of a memory operator increases the probability of a successful evolution for this particular problem. At this stage we make no claim for its impact on other problems that have been successfully addressed by Genetic Programming.

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Published date: 2000
Venue - Dates: Fourth International Conference on Adaptive Computing in Design and Manufacture (ACDM), 2000-04-01 - 2000-04-01

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Local EPrints ID: 21399
URI: https://eprints.soton.ac.uk/id/eprint/21399
ISBN: 9781852333003
PURE UUID: 6f9def6e-a383-4d78-8d02-ddc906421e5b

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Date deposited: 19 Feb 2007
Last modified: 08 Apr 2019 16:32

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