Linear Genetic Operators I — Segment Variations

Abstract

Crossover has been the traditional operator in tree-based GP for varying the content and size of programs. In this chapter we systematically introduce crossover and mutation operators for the linear program representation and compare their influence on prediction performance and the complexity of evolved solutions.

We can distinguish between two different levels of variation done by these operators. Macro variations operate on the instruction level (or macro level). In this perspective, an instruction represents the smallest unit. Micro variations operate on the level of instruction components (micro level) and manipulate registers, operators, and constants. Only macro variations influence program growth. Macro variations may be further divided into segment variations and instruction variations, depending on whether a contiguous subsequence of instructions or only one instruction is subjected to change. Only segment variations will form the subject of this chapter. Other variations will be treated in a subsequent chapter

We will see that the performance of a variation operator strongly depends on its maximum (and average) step size on the symbolic program structure, on its influence on code growth, and on the proportion of effective and neutral variations. Among other things, macro mutations with minimum step size will turn out to be most effective provided that a change of the structurally effective code can be guaranteed. We will also investigate how linear genetic programs can be manipulated more efficiently through respecting their functional structure.