scholarly journals Pruning of genetic programming trees using permutation tests

2020 ◽  
Vol 13 (4) ◽  
pp. 649-661
Author(s):  
Peter Rockett
Keyword(s):  
Genetic Programming ◽  
Permutation Tests ◽  
Test Function ◽  
Parent Tree ◽  
Novel Approach ◽  
Algebraic Simplification ◽  
Regression Problems ◽  
Statistical Change ◽  
Insight Into ◽  
Pruning Technique

Abstract We present a novel approach based on statistical permutation tests for pruning redundant subtrees from genetic programming (GP) trees that allows us to explore the extent of effective redundancy . We observe that over a range of regression problems, median tree sizes are reduced by around 20% largely independent of test function, and that while some large subtrees are removed, the median pruned subtree comprises just three nodes; most take the form of an exact algebraic simplification. Our statistically-based pruning technique has allowed us to explore the hypothesis that a given subtree can be replaced with a constant if this substitution results in no statistical change to the behavior of the parent tree—what we term approximate simplification. In the eventuality, we infer that more than 95% of the accepted pruning proposals are the result of algebraic simplifications, which provides some practical insight into the scope of removing redundancies in GP trees.

A New Method for Simplifying Algebraic Expressions in Genetic Programming Called Equivalent Decision Simplification

2009 ◽  
Vol 13 (3) ◽  
pp. 237-244 ◽  
Author(s):  
Naoki Mori ◽  
◽  
Bob McKay ◽  
Nguyen Xuan Hoai ◽  
Daryl Essam ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Genetic Programming ◽  
Symbolic Regression ◽  
New Method ◽  
Algebraic Expressions ◽  
Novel Approach ◽  
Key Issues ◽  
Regression Problems

Symbolic Regression is one of the most important applications of Genetic Programming, but suffers from one of the key issues in Genetic Programming, bloat. For a variety of reasons, reliable techniques to remove bloat are highly desirable. This paper introduces a novel approach of removing bloat, Equivalent Decision Simplification, in which subtrees are evaluated over the set of regression points. The effectiveness of the proposed method is confirmed by computer simulation taking simple Symbolic Regression problems as examples.

Anodic stripping voltammetry of anti-Vi antibody functionalized CdTe quantum dots for the specific monitoring of Salmonella enterica serovar Typhi

RSC Advances ◽  
2015 ◽  
Vol 5 (107) ◽  
pp. 88234-88240 ◽  
Author(s):  
Satish K. Pandey ◽  
Praveen Rishi ◽  
C. Raman Suri ◽  
Aaydha C. Vinayaka
Keyword(s):  
Quantum Dots ◽  
Capsular Polysaccharide ◽  
Anodic Stripping Voltammetry ◽  
Stripping Voltammetry ◽  
Cdte Quantum Dots ◽  
Salmonella Enterica Serovar Typhi ◽  
Novel Approach ◽  
Anodic Stripping ◽  
Vi Capsular Polysaccharide ◽  
Insight Into

CdTe QD based stripping voltammetry for Vi capsular polysaccharide detection. The technique has provided an insight into the competence of CdTe QD and GNP immuno-conjugates. This is a novel approach to characterize the efficiency of immuno-conjugates of QDs and GNPs.

scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

scholarly journals Exploring genetic programming systems with MAP-Elites

2018 ◽  
Author(s):  
Emily Dolson ◽  
Alexander Lalejini ◽  
Charles Ofria
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Pareto Front ◽  
Underlying Structure ◽  
Phenotypic Traits ◽  
Computation Technique ◽  
Phenotype Space ◽  
Programming Systems ◽  
Insight Into ◽  
Connection Cost

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.

Envelope wages as a new normal? An insight into a pool of prospective quasi-formal workers in the European Union (EU)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Josip Franic ◽  
Stanislaw Cichocki
Keyword(s):  
European Union ◽  
Pension System ◽  
Older Individuals ◽  
The European Union ◽  
Content Type ◽  
Random Intercept ◽  
Novel Approach ◽  
Wage Earners ◽  
Insight Into

PurposeIn spite of millions of quasi-formal workers in the European Union (EU), there is still limited understanding of what motivates workers to participate in these detrimental employment schemes, and why certain groups of workers exhibit higher inclination towards it. This article takes a novel approach by putting prospective envelope wage earners in the centre of this analysis.Design/methodology/approachData from the 2019 Special Eurobarometer on undeclared work are used, and two-level random intercept cumulative logit modelling is applied.FindingsOne in seven fully declared EU workers would have nothing against receiving one part of their wages off-the-books. Manual workers and individuals whose job assumes travelling are the most willing to accept such kind of remuneration, and the same applies to workers with low tax morale and those who perceive the risk of being detected and persecuted as very small. On the other hand, women, older individuals, married persons and employees from large enterprises express the smallest inclination towards envelope wages. The environment in which an individual operates also plays a non-negligible role as the quality of the pension system and the strength of social contract were also identified as significant determinants of workers' readiness to accept envelope wages.Originality/valueThis article fills in the gap in the literature by analysing what workers think about wage under-reporting and what factors drive their willingness to accept envelope wages.

scholarly journals Dysregulated Pyrimidine Biosynthesis Contributes to 5-FU Resistance in SCLC Patient-Derived Organoids but Response to a Novel Polymeric Fluoropyrimidine, CF10

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 788 ◽  
Author(s):  
William H. Gmeiner ◽  
Lance D. Miller ◽  
Jeff W. Chou ◽  
Anthony Dominijanni ◽  
Lysette Mutkus ◽  
...  
Keyword(s):  
Drug Target ◽  
Pyrimidine Biosynthesis ◽  
Cancer Drug ◽  
Rna Seq ◽  
Novel Approach ◽  
Anti Cancer ◽  
New Treatments ◽  
Insight Into ◽  
Sclc Patient ◽  
Identify Gene Expression

Chemo-immunotherapy is central to the treatment of small cell lung cancer (SCLC). Despite modest progress made with the addition of immunotherapy, current cytotoxic regimens display minimal survival benefit and new treatments are needed. Thymidylate synthase (TS) is a well-validated anti-cancer drug target, but conventional TS inhibitors display limited clinical efficacy in refractory or recurrent SCLC. We performed RNA-Seq analysis to identify gene expression changes in SCLC biopsy samples to provide mechanistic insight into the potential utility of targeting pyrimidine biosynthesis to treat SCLC. We identified systematic dysregulation of pyrimidine biosynthesis, including elevated TYMS expression that likely contributes to the lack of efficacy for current TS inhibitors in SCLC. We also identified E2F1-3 upregulation in SCLC as a potential driver of TYMS expression that may contribute to tumor aggressiveness. To test if TS inhibition could be a viable strategy for SCLC treatment, we developed patient-derived organoids (PDOs) from human SCLC biopsy samples and used these to evaluate both conventional fluoropyrimidine drugs (e.g., 5-fluorouracil), platinum-based drugs, and CF10, a novel fluoropyrimidine polymer with enhanced TS inhibition activity. PDOs were relatively resistant to 5-FU and while moderately sensitive to the front-line agent cisplatin, were relatively more sensitive to CF10. Our studies demonstrate dysregulated pyrimidine biosynthesis contributes to drug resistance in SCLC and indicate that a novel approach to target these pathways may improve outcomes.

scholarly journals The regression Tsetlin machine: a novel approach to interpretable nonlinear regression

2019 ◽  
Vol 378 (2164) ◽  
pp. 20190165 ◽  
Author(s):  
K. Darshana Abeyrathna ◽  
Ole-Christoffer Granmo ◽  
Xuan Zhang ◽  
Lei Jiao ◽  
Morten Goodwin
Keyword(s):  
Nonlinear Regression ◽  
Binary Representation ◽  
Propositional Formula ◽  
New Class ◽  
Text Understanding ◽  
Novel Approach ◽  
Continuous Output ◽  
Regression Techniques ◽  
Regression Problems ◽  
Continuous Input

Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

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