Multi Expression Programming -- an in-depth description

TL;DR

Multi Expression Programming (MEP) is a genetic programming variant that uses linear chromosomes to encode multiple solutions, enabling efficient evaluation and ensuring syntactic correctness without repair.

Contribution

This paper provides an in-depth description of MEP's linear representation, its ability to store multiple solutions, and its advantages over other genetic programming techniques.

Findings

MEP encodes multiple solutions in a single chromosome.

Evaluation of MEP expressions requires only one parsing.

Offspring are always syntactically correct, eliminating repair steps.

Abstract

Multi Expression Programming (MEP) is a Genetic Programming variant that uses a linear representation of chromosomes. MEP individuals are strings of genes encoding complex computer programs. When MEP individuals encode expressions, their representation is similar to the way in which compilers translate $C$ or $Pascal$ expressions into machine code. A unique MEP feature is the ability to store multiple solutions of a problem in a single chromosome. Usually, the best solution is chosen for fitness assignment. When solving symbolic regression or classification problems (or any other problems for which the training set is known before the problem is solved) MEP has the same complexity as other techniques storing a single solution in a chromosome (such as GP, CGP, GEP or GE). Evaluation of the expressions encoded into an MEP individual can be performed by a single parsing of the chromosome. Offspring obtained by crossover and mutation is always syntactically correct MEP individuals (computer programs). Thus, no extra processing for repairing newly obtained individuals is needed.