%0 Conference Paper %1 wilson:2003:ccpb %A Wilson, G. C. %A Heywood, M. I. %B IEEE CCECE 2002: IEEE Canadian Conference on Electrical and Computer Engineering %D 2002 %E Kinsner, W. %E Seback, A. %E Ferens, K. %I IEEE Press %K (artificial Learning, Mateo San Strategy algorithms, ants, artificial based change, code crossover effective fitness genetic instructions, intelligence), learning memories, operator, problems, programming, register search sequences, simple strategies, strategy trail, %P 809--814 %T Crossover Context in Page-based Linear Genetic Programming %U http://flame.cs.dal.ca/~gwilson/docs/papers/ccece_2002.pdf %V 2 %X This work explores strategy learning through genetic programming in artificial ants that navigate the San Mateo trail. We investigate several properties of linearly structured (as opposed to typical tree based) GP including: the significance of simple register based memories, the significance of constraints applied to the crossover operator, and how active the ant are. We also provide a basis for investigating more thoroughly the relation between specific code sequences and fitness by dividing the genome into pages of instructions and introducing an analysis of fitness change and exploration of the trail done by particular parts of a genome. By doing so we are able to present results on how best to find the instructions in an individual's program that contribute positively to the accumulation of effective search strategies. %@ 0-7803-7515-7

@inproceedings{wilson:2003:ccpb, abstract = {This work explores strategy learning through genetic programming in artificial ants that navigate the San Mateo trail. We investigate several properties of linearly structured (as opposed to typical tree based) GP including: the significance of simple register based memories, the significance of constraints applied to the crossover operator, and how active the ant are. We also provide a basis for investigating more thoroughly the relation between specific code sequences and fitness by dividing the genome into pages of instructions and introducing an analysis of fitness change and exploration of the trail done by particular parts of a genome. By doing so we are able to present results on how best to find the instructions in an individual's program that contribute positively to the accumulation of effective search strategies.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Wilson, G. C. and Heywood, M. I.}, biburl = {https://www.bibsonomy.org/bibtex/22c72016f4f2ca6fc4b01d5cc1ef4873e/brazovayeye}, booktitle = {IEEE CCECE 2002: IEEE Canadian Conference on Electrical and Computer Engineering}, editor = {Kinsner, W. and Seback, A. and Ferens, K.}, interhash = {3198643db5b89a35cca1e38cb3718c10}, intrahash = {2c72016f4f2ca6fc4b01d5cc1ef4873e}, isbn = {0-7803-7515-7}, issn = {0840-7789}, keywords = {(artificial Learning, Mateo San Strategy algorithms, ants, artificial based change, code crossover effective fitness genetic instructions, intelligence), learning memories, operator, problems, programming, register search sequences, simple strategies, strategy trail,}, month = {12-15 May}, notes = {best student paper award}, organisation = {IEEE Canada}, pages = {809--814}, publisher = {IEEE Press}, size = {6 pages}, timestamp = {2008-06-19T17:54:13.000+0200}, title = {Crossover Context in Page-based Linear Genetic Programming}, url = {http://flame.cs.dal.ca/~gwilson/docs/papers/ccece_2002.pdf}, volume = 2, year = 2002 }