Improving Performance of GP by Adaptive Terminal Selection

Ok, Soo-Yol; Miyashita, Kazuo; Nishihara, Seiichi

doi:10.1007/3-540-44533-1_45

Cited by 15 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we reached a complexity barrier for the evolutionary process; too many terminals lead to an explosion of the search space resulting in a problem that is too hard to solve with a standard general purpose PC in an acceptable time span. This is a concern that is shared by other authors in the field as well [15].…”

Section: Resultsmentioning

confidence: 96%

Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming

Bonte

Wyns

2010

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Abstract. Genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. In this contribution we use genetic programming to automatically evolve efficient robot controllers for a corridor following task. Based on tests executed in a simulation environment we show that very robust and efficient controllers can be obtained. Also, we stress that it is important to provide sufficiently diverse fitness cases, offering a sound basis for learning more complex behaviour. The evolved controller is successfully applied to real environments as well. Finally, controller and sensor morphology are co-evolved, clearly resulting in an improved sensor configuration.

show abstract

Section: Resultsmentioning

confidence: 96%

Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming

Bonte

Wyns

2010

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

show abstract

“…During each generation, the feature weight vector is updated while considering all individuals in the population. Similar work proposed previously by Ok et al [Ok et al 2000] where only the top 10% of the solutions are considered when computing the feature weight vector. However, in their approach, re-initialization of the original feature space is performed to omit irrelevant terminal variables.…”

Section: 22mentioning

confidence: 85%

Identity verification based on handwritten signatures with haptic information using genetic programming

Alsulaiman

Sakr

Valdés

et al. 2013

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

In this paper, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including Support Vector Machine, k-Nearest Neighbors, Naïve Bayes and Random Forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).

show abstract

“…In GP, Ok et al [17] proposed an adaptive terminal selection scheme and corresponding adaptive mutation based on the updated terminal set. Friedlander et al [3] proposed a feature ranking method based on frequency analysis.…”

Section: Feature Selectionmentioning

confidence: 99%

Feature selection in evolving job shop dispatching rules with genetic programming

Yi¹,

Zhang²,

Nyugen³

2021

Preprint

View full text Add to dashboard Cite

Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances. © Mei 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/2908812.2908822.

show abstract

Improving Performance of GP by Adaptive Terminal Selection

Cited by 15 publications

References 1 publication

Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming

Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming

Identity verification based on handwritten signatures with haptic information using genetic programming

Feature selection in evolving job shop dispatching rules with genetic programming

Contact Info

Product

Resources

About