Genetic programming reconsidered

Artificial Intelligence Review, 2002

Generalisation is one of the most important performance evaluation criteria for artificial learning systems. An increasing amount of research has recently concentrated on the robustness or generalisation ability of the programs evolved using Genetic Programming (GP). While some of these researchers report on the brittleness of the solutions evolved, some others propose methods of promoting robustness/generalisation. In this paper, a review of research on generalisation in GP and problems with brittleness of solutions produced by GP is presented. Also, a brief overview of several new methods promoting robustness/generalisation of the solutions produced by GP are presented.

2009

• Papers developing techniques tested on small-scale problems include discussion of how to apply those techniques to real-world problems, while papers tackling real-world problems have employed techniques developed from theoretical work to gain insights.

Genetic and Evolutionary Computation, 2011

, except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

… , IEEE Transactions on, 1999

2003

Engineered Biomimicry, 2013

We discuss Evolutionary Computation, in particular Genetic Programming, as examples of drawing inspiration from biological systems. We set the choice of evolution as a source for inspiration in context, discuss the history of Evolutionary Computation and its variants before looking more closely at Genetic Programming. After a discussion of methods and the state-of-theart, we review application areas of Genetic Programming and its strength in providing human-competitive solutions.

Evolutionary Computation, 1999

Genetic and Evolutionary Computation Series, 2008

, except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

2006

This paper investigates developmental evaluation in Genetic Programming (GP). Extant GP systems, including developmental GP systems, typically exhibit modular and hierarchical structure only to the degree it is built-in by the designer; by contrast, biological systems exhibit a high degree of organization in their genotypes. We hypothesise that even when GP systems are subject to changing environments, for which the adaptability arising from modular structure would be advantageous, the benefit is at the species rather than individual level, so that selection is very weak. By contrast, biological systems are selected repeatedly throughout their development process. We suggest that this difference is crucial; that if an individual is evaluated multiple times throughout its development, then modular structure can provide an adaptive advantage to that individual, and hence can be selected for by evolution. We investigate this hypothesis using Tree Adjoining Grammar Guided Genetic Programming (TAG3P) [1], which has good properties for supporting evaluation during incremental development. Our preliminary results show that developmental TAG3P outperforms both original TAG3P and standard tree-based GP on an appropriate problem, in ways which suggest that modular solutions may have been developed.