GEF: A Self-Programming Robot Using Grammatical Evolution

نویسندگان

  • Charles Peabody
  • Jennifer Seitzer
چکیده

Grammatical Evolution (GE) is that area of genetic algorithms that evolves computer programs in high-level languages possessing a BNF grammar. In this work, we present GEF (“Grammatical Evolution for the Finch”), a system that employs grammatical evolution to create a Finch robot controller program in Java. The system uses both the traditional GE model as well as employing extensions and augmentations that push the boundaries of goal-oriented contexts in which robots typically act including a meta-level handler that fosters a level of selfawareness in the robot. To handle contingencies, the GEF system has been endowed with the ability to perform meta-level jumps. When confronted with unplanned events and dynamic changes in the environment, our robot will automatically transition to pursue another goal, changing fitness functions, and generate and invoke operating system level scripting to facilitate the change. The robot houses a raspberry pi controller that is capable of executing one (evolved) program while wirelessly receiving another over an asynchronous client. This work is part of an overall project that involves planning for contingencies. In this poster, we present the development framework and system architecture of GEF, including the newly discovered meta-level handler, as well as some other system successes, failures, and insights.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Method for Solving Optimal Control Problems Using Genetic Programming

This paper deals with a novel method for solving optimal control problems based on genetic programming. This approach produces some trial solutions and seeks the best of them. If the solution cannot be expressed in a closed analytical form then our method produces an approximation with a controlled level of accuracy. Using numerical examples, we will demonstrate how to use the results.

متن کامل

Analytic Programming – Symbolic Regression by Means of Arbitrary Evolutionary Algorithms

This contribution introduces analytical programming, a novel method that allows solving various problems from the symbolic regression domain. Symbolic regression was first proposed by J. R. Koza in his genetic programming and by C. Ryan in grammatical evolution. This contribution explains the main principles of analytic programming, and demonstrates its ability to synthesize suitable solutions,...

متن کامل

A Comparative Study of Genetic Programming and Grammatical Evolution for Evolving Data Structures

The research presented in the paper forms part of a larger initiative aimed at automatic algorithm induction using machine learning. This paper compares the performance of two machine learning techniques, namely, genetic programming and a variation of genetic programming, grammatical evolution, for automatic algorithm induction. The application domain used to evaluate both the approaches is the...

متن کامل

Grammatical Differential Evolution

This proof of concept study examines the possibility of specifying the construction of programs using Differential Evolution, and represents a new form of grammar-based genetic programming, Grammatical Differential Evolution (GDE). In GDE each individual member of the population represents a specific choice of program construction rules, where these rules are specified using a Backus-Naur Form ...

متن کامل

A Genetic Programming-based Scheme for Solving Fuzzy Differential Equations

This paper deals with a new approach for solving fuzzy differential equations based on genetic programming. This method produces some trial solutions and seeks the best of them. If the solution cannot be expressed in a closed analytical form then our method produces an approximation with a controlled level of accuracy. Furthermore, the numerical results reveal the potential of the proposed appr...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015