James Maclaurin

This paper develops an account of evolutionary progress for use in the field of evolutionary economics. Previous work is surveyed and a new account set out, based on the idea of evolvability as it has been used recently in evolutionary developmental biology. The biological underpinnings of this idea are explained using examples of a series of phenomena that influence the evolvability of biological systems. It is further argued that selection pressures and developmental processes are sufficiently similar to make this biological concept useful in economics. The new account is defended against a number of common objections to the notion of progress in evolving systems, including the claim that all stipulated measures of evolutionary progress are essentially arbitrary. It is argued that progress, understood as an increase in evolvability over time, is both philosophically well-justified and provides useful predictive and explanatory resources to those seeking to understand and manipulate evolving economic systems.

Grammatical Evolution (GE) has a long history in evolutionary computation. Central to the behaviour of GE is the use of a linear representation and grammar to map individuals from search spaces into problem spaces. This genotype to phenotype mapping is often argued as a distinguishing property of GE relative to other techniques, such as context-free grammar genetic programming (CFG-GP). Since its initial description, GE research has attempted to incorporate information from the grammar into crossover, mutation, and individual initialisation, blurring the distinction between genotype and phenotype and creating GE variants closer to CFG-GP. This is argued to provide GE with the "best of both worlds", allowing degrees of grammatical bias to be introduced into operators to best suit the given problem. This paper examines the behaviour of three grammar-based search methods on several problems from previous GE research. It is shown that, unlike CFG-GP, the performance of "pure" GE on the examined problems closely resembles that of random search. The results suggest that further work is required to determine the cases where the "best of both worlds" of GE are required over a straight CFG-GP approach.