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Related Experiment Video

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

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Published on: October 14, 2017

A simple approach to lifetime learning in genetic programming-based symbolic regression.

Raja Muhammad Atif Azad¹, Conor Ryan

¹CSIS Department, University of Limerick, Ireland atif.azad@ul.ie.

Evolutionary Computation

|October 2, 2013

Summary

Genetic programming (GP) now includes lifetime learning with the Chameleon system, enhancing computer program evolution. This approach improves efficiency and performance over standard GP by incorporating local search and caching mechanisms.

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Area of Science:

Computer Science
Artificial Intelligence
Evolutionary Computation

Background:

Genetic programming (GP) models natural evolution to create computer programs.
GP individuals typically lack lifetime learning or the ability to pass on acquired knowledge, unlike biological organisms.
Existing GP methods often have static fitness throughout an individual's lifespan.

Purpose of the Study:

Introduce the Chameleon system to augment GP with lifetime learning capabilities.
Address the discrepancy between GP and natural evolution regarding acquired knowledge and experience.
Enhance the efficiency and performance of genetic programming through novel mechanisms.

Main Methods:

Implement a simple local search mechanism within GP that tunes internal nodes of individuals.
Incorporate a caching strategy to reduce the computational cost of local search.
Provide a theoretical upper bound on tuning expense relative to population characteristics.

Main Results:

Chameleon demonstrates more active exploration, utilizing genetic material more efficiently than standard GP.
The system outperforms standard GP on symbolic regression problems, both in training and testing.
Chameleon produces smaller individuals compared to standard GP while achieving superior performance.

Conclusions:

The Chameleon system effectively integrates lifetime learning into genetic programming.
Chameleon offers a computationally inexpensive and easy-to-implement enhancement to GP.
Chameleon shows compatibility with other GP extensions like linear scaling and diversity-promoting selection.