Results 1  10
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12
Repeated sequences in linear genetic programming genomes
 Complex Systems
"... Biological chromosomes are replete with repetitive sequences, microsatellites, SSR tracts, ALU, and so on, in their DNA base sequences. We started looking for similar phenomena in evolutionary computation. First studies find copious repeated sequences, which can be hierarchically decomposed into sho ..."
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Cited by 16 (9 self)
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Biological chromosomes are replete with repetitive sequences, microsatellites, SSR tracts, ALU, and so on, in their DNA base sequences. We started looking for similar phenomena in evolutionary computation. First studies find copious repeated sequences, which can be hierarchically decomposed into shorter sequences, in programs evolved using both homologous and twopoint crossover but not with headless chicken crossover or other mutations. In bloated programs the small number of effective or expressed instructions appear in both repeated and nonrepeated code. Hinting that buildingblocks or code reuse may evolve in unplanned ways. Mackey–Glass chaotic time series prediction and eukaryotic protein localization (both previously used as artificial intelligence machine learning benchmarks) demonstrate the evolution of Shannon information (entropy) and lead to models capable of lossy Kolmogorov compression. Our findings with diverse benchmarks and genetic programming (GP) systems suggest this emergent phenomenon may be widespread in genetic systems. “DNA whose sequence is not maintained by selection will develop periodicities as a result of random crossover.” George P. Smith [17]. 1.
The halting probability in von Neumann architectures
 Proceedings of the 9th European Conference on Genetic Programming, volume 3905 of Lecture
, 2006
"... Abstract. Theoretical models of Turing complete linear genetic programming (GP) programs suggest the fraction of halting programs is vanishingly small. Convergence results proved for an idealised machine, are tested on a small T7 computer with (finite) memory, conditional branches and jumps. Simulat ..."
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Cited by 14 (4 self)
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Abstract. Theoretical models of Turing complete linear genetic programming (GP) programs suggest the fraction of halting programs is vanishingly small. Convergence results proved for an idealised machine, are tested on a small T7 computer with (finite) memory, conditional branches and jumps. Simulations confirm Turing complete fitness landscapes of this type hold at most a vanishingly small fraction of usable solutions. 1
How many Good Programs are there? How Long are they?
, 2002
"... We model the distribution of functions implemented by nonrecursive programs, similar to linear genetic programming (GP). Most functions are constants, the remainder are mostly parsimonious. The effect of adhoc rules on GP are described and new heuristics are proposed. Bounds on how long programs n ..."
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Cited by 14 (8 self)
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We model the distribution of functions implemented by nonrecursive programs, similar to linear genetic programming (GP). Most functions are constants, the remainder are mostly parsimonious. The effect of adhoc rules on GP are described and new heuristics are proposed. Bounds on how long programs need to be before the distribution of their functionality is close to its limiting distribution are provided in general and for average computers. Results for average computers and a model like genetic programming are experimentally tested.
The distribution of reversible functions is Normal
 In Genetic Programming Theory and Practise
, 2003
"... The distribution of reversible programs tends to a limit as their size increases. For problems with a Hamming distance fitness function the limiting distribution is binomial with an exponentially small chance (but non zero) chance of perfect solution. Sufficiently good reversible circuits are more c ..."
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Cited by 9 (6 self)
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The distribution of reversible programs tends to a limit as their size increases. For problems with a Hamming distance fitness function the limiting distribution is binomial with an exponentially small chance (but non zero) chance of perfect solution. Sufficiently good reversible circuits are more common. Expected RMS error is also calculated. Random unitary matrices may suggest possible extension to quantum computing. Using the genetic programming (GP) benchmark, the six multiplexor, circuits of Toffoli gates are shown to give a fitness landscape amenable to evolutionary search. Minimal CCNOT solutions to the six multiplexer are found but larger circuits are more evolvable.
Convergence of Program Fitness Landscapes
 GECCO 2003, LNCS 2724
, 2003
"... Point mutation has no effect on almost all linear programs. In two genetic ..."
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Cited by 8 (4 self)
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Point mutation has no effect on almost all linear programs. In two genetic
Repeated sequences in linear gp genomes
 In Late breaking paper at GECCO’2004
, 2004
"... Abstract. Biological chromosomes are replete with repetitive sequences, microsatellites, SSR tracts, ALU, etc. in their DNA base sequences. We discover hierarchical repeating sequences (building blocks?) are evolved by genetic programming in linear time series prediction programs. “DNA whose sequenc ..."
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Cited by 5 (0 self)
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Abstract. Biological chromosomes are replete with repetitive sequences, microsatellites, SSR tracts, ALU, etc. in their DNA base sequences. We discover hierarchical repeating sequences (building blocks?) are evolved by genetic programming in linear time series prediction programs. “DNA whose sequence is not maintained by selection will develop periodicities as a result of random crossover ” George P Smith, Science, 1976. 1
On Turing complete T7 and MISC F4 program fitness landscapes
 Computer Science, University of Essex
, 2006
"... We use the minimal instruction set F4 computer to define a minimal Turing complete T7 computer suitable for genetic programming (GP) and amenable to theoretical analysis. Experimental runs and mathematical analysis of the T7, show the fraction of halting programs is drops to zero as bigger programs ..."
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Cited by 4 (4 self)
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We use the minimal instruction set F4 computer to define a minimal Turing complete T7 computer suitable for genetic programming (GP) and amenable to theoretical analysis. Experimental runs and mathematical analysis of the T7, show the fraction of halting programs is drops to zero as bigger programs are run. 1
Efficient Markov chain model of machine code program execution and halting
, 2006
"... This paper focuses on the halting probability and the number of instructions executed by programs that halt for Turingcomplete assemblylike languages and register based machines. ..."
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Cited by 4 (4 self)
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This paper focuses on the halting probability and the number of instructions executed by programs that halt for Turingcomplete assemblylike languages and register based machines.
Mapping Nonconventional Extensions of Genetic Programming
"... Abstract. Conventional genetic programming research excludes memory and iteration. We have begun an extensive analysis of the space through which GP or other unconventional AI approaches search and extend it to consider explicit program stop instructions (T8) and any time models (T7). We report halt ..."
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Cited by 3 (3 self)
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Abstract. Conventional genetic programming research excludes memory and iteration. We have begun an extensive analysis of the space through which GP or other unconventional AI approaches search and extend it to consider explicit program stop instructions (T8) and any time models (T7). We report halting probability, run time and functionality (including entropy of binary functions) of both halting and anytime programs. Turing complete program fitness landscapes, even with halt, scale poorly. 1
Analysis of genetic programming runs
 AsiaPacific Workshop on Genetic Programming
, 2004
"... We have analysed runs of 12 different genetic programming problems. Some of the problems are the ‘toy ’ problems used in generic programming research and some are significant real world applications. We have generated log files of the runs and looked for recurring and unusual patterns and whether th ..."
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Cited by 2 (1 self)
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We have analysed runs of 12 different genetic programming problems. Some of the problems are the ‘toy ’ problems used in generic programming research and some are significant real world applications. We have generated log files of the runs and looked for recurring and unusual patterns and whether there are any differences between the toy problems and the real world problems. The major finding is that some programs are being evaluated many times. In the realworld problems 3078 % of the time was spent on reevaluating programs that had already been evaluated. For problems where the evaluation function is expensive significant savings are possible if evaluated programs are cached. A surprising finding was that, for two of the real world problems, a very large number of the evaluations were of 1node programs. 1.