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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Instruction-matrix-based genetic programming.

Gang Li¹, Jin Feng Wang, Kin Hong Lee

¹Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|July 18, 2008

Summary

This summary is machine-generated.

Instruction-matrix (IM)-based genetic programming (GP) evolves tree nodes separately, improving solution quality and efficiency. This novel IMGP framework outperforms canonical GP and other related algorithms in benchmark tests and classification tasks.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Genetic Programming (GP) faces challenges with large solution spaces due to interdependent tree nodes.
Evolving tree nodes separately in GP can reduce computational complexity but requires handling fitness interdependencies.

Purpose of the Study:

To introduce a novel Instruction-Matrix (IM)-based Genetic Programming (IMGP) framework to manage interactions between separately evolved tree nodes.
To enhance the effectiveness and efficiency of genetic programming by directly evolving schemata.

Main Methods:

IMGP utilizes an instruction matrix (IM) to evolve tree nodes and subtrees independently.
Program trees are extracted from the IM, and the IM is updated based on the extracted tree information, facilitating direct schemata evolution.

Main Results:

IMGP demonstrated superior solution quality and fewer program evaluations compared to canonical GP and related algorithms on benchmark problems.
Classifiers evolved using IMGP achieved higher classification accuracies than four other GP algorithms on benchmark datasets.
Testing errors for IMGP classifiers were comparable to or better than those of established classifiers.

Conclusions:

IMGP offers an effective and efficient approach to genetic programming by directly evolving schemata through an instruction matrix.
The IMGP framework shows significant promise for both general problem-solving and classification tasks, including multiclass problems with its extended version.