Predicting Prime Numbers Using Cartesian Genetic Programming

Walker, James Alfred; Miller, Julian F.

doi:10.1007/978-3-540-71605-1_19

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…Mackey-Glass Chaotic Time Series [35] 8 Y N Y N Sunspot Prediction [27] 12 Y N Y N Financial Trading [6,13] 75-273 Y N Y Y Prime Number Prediction [66] 1 Y N Y N Drug Bioavailability [57] 241 Y N Y N Median Oral Lethal Dose [2] 626 Y N Y N Time Series Forecasting [65] 12-240 Y N Y N…”

Section: Predictive Modellingmentioning

confidence: 99%

Genetic programming needs better benchmarks

McDermott

White

Luke

et al. 2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

237

173

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Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its benchmark problems are popular purely through historical contingency, and they can be criticized as too easy or as providing misleading information concerning real-world performance, but they persist largely because of inertia and the lack of good alternatives. Even where the problems themselves are impeccable, comparisons between studies are made more difficult by the lack of standardization. We argue that the definition of standard benchmarks is an essential step in the maturation of the field. We make several contributions towards this goal. We motivate the development of a benchmark suite and define its goals; we survey existing practice; we enumerate many candidate benchmarks; we report progress on reference implementations; and we set out a concrete plan for gathering feedback from the GP community that would, if adopted, lead to a standard set of benchmarks.

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Section: Predictive Modellingmentioning

confidence: 99%

Genetic programming needs better benchmarks

McDermott

White

Luke

et al. 2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

237

173

View full text Add to dashboard Cite

show abstract

“…There was again support for signal processing, algorithmic and synthetic problems. Other suggested problems rarely mentioned elsewhere included prime number prediction [52] and real-valued optimization. One respondent recommended Boolean problems, but suggested that better ones than the typical parity and multiplexer problems were required, such as the multiple output parallel multiplier [53].…”

Section: Composition Of a New Benchmark Suitementioning

confidence: 99%

Better GP benchmarks: community survey results and proposals

White

McDermott

Castelli

et al. 2012

Genet Program Evolvable Mach

200

135

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We present the results of a community survey regarding genetic programming benchmark practices. Analysis shows broad consensus that improvement is needed in problem selection and experimental rigor. While views expressed in the survey dissuade us from proposing a large-scale benchmark suite, we find community support for creating a ''blacklist'' of problems which are in common use but have important flaws, and whose use should therefore be discouraged. We propose a set of possible replacement problems.

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“…• The prime generating polynomial problem where the goal is to discover a polynomial that generates as many different primes in a row as possible. Cartesian Genetic Programming has also been applied to the problem of finding polynomials that generate prime numbers [21].…”

Section: Monte Carlo Expression Discoverymentioning

confidence: 99%

Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery

Cazenave

Hamida

2015

2015 IEEE Symposium Series on Computational Intelligence

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We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learn from financial time series to generate non linear functions for market volatility prediction. The input data, that is a series of daily prices of European S&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples.

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Predicting Prime Numbers Using Cartesian Genetic Programming

Cited by 13 publications

References 11 publications

Genetic programming needs better benchmarks

Genetic programming needs better benchmarks

Better GP benchmarks: community survey results and proposals

Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery

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