Linear Genetic Programming

This section provides background information regarding Linear Genetic Programming (LGP) which is necessary to get started using this system. The concepts used in LGP as described here are given by Brameier, M. F., & Banzhaf, W. (2007) [1].

It should be noted that this section is not designed to be a complete reference for LGP, it merely gives enough information to get started using this system.

The fundamental concept of Genetic Programming (GP) borrows ideas from evolution in order to take a set of randomly generated programs and train them towards a particular target function through selection, reproduction, and mutation.

The process has a large amount of randomness to it, in that the initial program solutions are random and then those initial programs are randomly selected, reproduced and mutated to form other solutions.

Contents:

• Representation
  • Programs
  • Registers
  • Instruction Set
  • Control Flow
• Evolution
  • Evolutionary Algorithm
  • Initialisation
  • Selection
  • Variation
• Execution
  • Evaluation
  • Translation

[1]	Brameier, M. F., & Banzhaf, W. (2007). Linear Genetic Programming. Springer Science & Business Media. https://doi.org/10.1007/978-0-387-31030-5