Recent Patents on Genetic Programming

McDermott

Swafford

et al. 2010

IJDE

Self Cite

Publication informationInternational Journal of Design Engineering, 3 (1): 4-24 Publisher Inderscience EnterprisesLink to online version http://dx. Martin Hemberg is a post-doctoral researcher at the Department of Ophthalmology at Children's Hospital Boston. He obtained is PhD from Imperial College London and he has also worked at the Architectural Association in London. His primary research interests include matheEvolutionary design using grammatical evolution and shape grammars 3 matical and computational models of gene expression

Section: Introductionmentioning

confidence: 99%

Evolutionary design using grammatical evolution and shape grammars: designing a shelter

McDermott

Swafford

et al. 2010

IJDE

Self Cite

“…We are now starting to benefit from this as anecdotal evidence, and indeed personal experience of some of the authors of this paper, suggests that GP is increasingly being adopted in a diverse array of commercial settings including design and engineering, telecommunications and networks, gaming and finance. The number and variety of recent patent applications which adopt GP also provides tangible evidence of its increased adoption and recognition [95].…”

mentioning

confidence: 99%

Open issues in genetic programming

Genet Program Evolvable Mach

Vanneschi

Gustafson

et al. 2010

190

It is approximately fifty years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two decades there has been a significant range and volume of development in the theory and application of GP, and in recent years the field has become increasingly applied. There remain a number of significant open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in the development of a theory explaining the behavior and dynamics of GP. These issues must be addressed for GP to realise its full potential and to become a trusted mainstream member of the computational problem solving toolkit. In this paper we outline some of the challenges and open issues that face researchers and practitioners of GP. We hope this overview will stimulate debate, focus the direction of future research to deepen our understanding of GP, and further the development of more powerful problem solving algorithms.

Dynamic trade execution: a grammatical evolution approach

Cui

Brabazon

2011

IJFMD

Self Cite

Abstract:Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument. Investors wishing to execute large orders face a tradeoff between market impact and opportunity cost. Trade execution strategies are designed to balance out these costs, thereby minimising total trading cost. Despite the importance of optimising the trade execution process, this is difficult to do in practice due to the dynamic nature of markets and due to our imperfect understanding of them. In this paper, we adopt a novel approach, combining an evolutionary methodology whereby we evolve high-quality trade execution strategies, with an agent-based artificial stock market, wherein the evolved strategies are tested. The evolved strategies are found to outperform a series of benchmark strategies and several avenues are suggested for future work.