This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.

Automatic Re-engineering of Software Using Genetic Programming describes the application of Genetic Programming to a real world application area - software re-engineering in general and automatic parallelization specifically. Unlike most uses of Genetic Programming, this book evolves sequences of provable transformations rather than actual programs. It demonstrates that the benefits of this approach are twofold: first, the time required for evaluating a population is drastically reduced, and second, the transformations can subsequently be used to prove that the new program is functionally equivalent to the original. Automatic Re-engineering of Software Using Genetic Programming shows that there are applications where it is more practical to use GP to assist with software engineering rather than to entirely replace it. It also demonstrates how the author isolated aspects of a problem that were particularly suited to GP, and used traditional software engineering techniques in those areas for which they were adequate. Automatic Re-engineering of Software Using Genetic Programming is an excellent resource for researchers in this exciting new field.

This handbook offers a comprehensive treatise on Grammatical Evolution (GE), a grammar-based Evolutionary Algorithm that employs a function to map binary strings into higher-level structures such as programs. GE's simplicity and modular nature make it a very flexible tool. Since its introduction almost twenty years ago, researchers have applied it to a vast range of problem domains, including financial modelling, parallel programming and genetics. Similarly, much work has been conducted to exploit and understand the nature of its mapping scheme, triggering additional research on everything from different grammars to alternative mappers to initialization. The book first introduces GE to the novice, providing a thorough description of GE along with historical key advances. Two sections follow, each composed of chapters from international leading researchers in the field. The first section concentrates on analysis of GE and its operation, giving valuable insight into set up and deployment. The second section consists of seven chapters describing radically different applications of GE. The contributions in this volume are beneficial to both novices and experts alike, as they detail the results and researcher experiences of applying GE to large scale and difficult problems. Topics include: * Grammar design * Bias in GE * Mapping in GE * Theory of disruption in GE * Structured GE * Geometric semantic GE * GE and semantics * Multi- and Many-core heterogeneous parallel GE * Comparing methods to creating constants in GE * Financial modelling with GE * Synthesis of parallel programs on multi-cores * Design, architecture and engineering with GE * Computational creativity and GE * GE in the prediction of glucose for diabetes * GE approaches to bioinformatics and system genomics * GE with coevolutionary algorithms in cybersecurity * Evolving behaviour trees with GE for platform games * Business analytics and GE for the prediction of patient recruitment in multicentre clinical trials

Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language provides the first comprehensive introduction to Grammatical Evolution, a novel approach to Genetic Programming that adopts principles from molecular biology in a simple and useful manner, coupled with the use of grammars to specify legal structures in a search. Grammatical Evolution's rich modularity gives a unique flexibility, making it possible to use alternative search strategies - whether evolutionary, deterministic or some other approach - and to even radically change its behavior by merely changing the grammar supplied. This approach to Genetic Programming represents a powerful new weapon in the Machine Learning toolkit that can be applied to a diverse set of problem domains.

A thorough examination of the characteristics` belonging to a high-performing school, this book is written by the Chairman of the Specialist Schools Trust and the education advisor to the Prime Minister, Tony Blair. It draws on numerous case studies of successful schools, as well as showing how previously failing schools have been turned around. Looking at such areas as leadership, staffing, target-setting, discipline and order, curriculum innovation and individual learning, the book offers a blueprint to head teachers and others trying to develop excellent schools.

Excellence in Education: the making of great schools tells the side of the story of education in England not often portrayed in the media - one that celebrates the success of the many schools that have experienced dramatic improvements in the last five years. The authors discuss all the elements that have contributed to these improvements including: improving the quality of the leadership team; the setting of targets and use of data; curriculum innovation; the use of ICT; improved discipline and order; the designation of nearly 2,000 schools as specialist schools. Primarily about the experience of secondary schools - although full of techniques and case studies relevant to primary schools - this book will serve as a manual for school improvement.

A thorough examination of the characteristics of a high- performing school, written by Sir Cyril Taylor (Chairman of the Specialist Schools Trust) and Conor Ryan (senior adviser to Tony Blair on Education).

This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.

Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language provides the first comprehensive introduction to Grammatical Evolution, a novel approach to Genetic Programming that adopts principles from molecular biology in a simple and useful manner, coupled with the use of grammars to specify legal structures in a search. Grammatical Evolution's rich modularity gives a unique flexibility, making it possible to use alternative search strategies - whether evolutionary, deterministic or some other approach - and to even radically change its behavior by merely changing the grammar supplied. This approach to Genetic Programming represents a powerful new weapon in the Machine Learning toolkit that can be applied to a diverse set of problem domains.

Automatic Re-engineering of Software Using Genetic Programming describes the application of Genetic Programming to a real world application area - software re-engineering in general and automatic parallelization specifically. Unlike most uses of Genetic Programming, this book evolves sequences of provable transformations rather than actual programs. It demonstrates that the benefits of this approach are twofold: first, the time required for evaluating a population is drastically reduced, and second, the transformations can subsequently be used to prove that the new program is functionally equivalent to the original. Automatic Re-engineering of Software Using Genetic Programming shows that there are applications where it is more practical to use GP to assist with software engineering rather than to entirely replace it. It also demonstrates how the author isolated aspects of a problem that were particularly suited to GP, and used traditional software engineering techniques in those areas for which they were adequate. Automatic Re-engineering of Software Using Genetic Programming is an excellent resource for researchers in this exciting new field.

This book constitutes the refereed proceedings of the 6th European Conference on Genetic Programming, EuroGP 2003, held in Essex, UK in April 2003. The 45 revised papers presented were carefully reviewed and selected from 61 submissions. All current aspects of genetic programming and genetic algorithms are addressed, ranging from foundational, theoretical, and methodological issues to advanced applications in various fields.

This book constitutes the refereed proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science, AICS 2002, held in Limerick, Ireland in September 2002.The 16 revised full papers and 17 revised short papers presented were carefully reviewed and selected for inclusion in the book. Among the topics addressed are cognitive modeling, case-based reasoning, constraint processing, data mining, evolutionary computation, intelligent agents, information retrieval, knowledge representation, reasoning, machine learning, natural language processing, neural networks, perception, AI planning, robotics, and scheduling.

This book constitutes the refereed proceedings of the 5th European Conference on Genetic Programming, EuroGP 2002, held in Kinsale, Ireland, in April 2002.The 18 revised full papers and 14 posters presented were carefully reviewed and selected from 42 submissions. All current aspects of genetic programming and genetic algorithms are addressed, ranging from theoretical and foundational issues to applications in a variety of fields.

This book constitutes the refereed proceedings of the 4th European Conference on Genetic Programming, EuroGP 2001, held at Lake Como, Italy in April 2001.The 17 revised full papers and 13 research posters presented were carefully reviewed and selected during a rigorous double-blind refereeing process out of 42 submissions. All current aspects of genetic programming are addressed, ranging from theoretical and foundational issues to applications in a variety of fields such as robotics, artificial retina, character recognition, financial prediction, digital filter and electronic circuit design, image processing, data fusion, and bio-sequencing.

This handbook offers a comprehensive treatise on Grammatical Evolution (GE), a grammar-based Evolutionary Algorithm that employs a function to map binary strings into higher-level structures such as programs. GE's simplicity and modular nature make it a very flexible tool. Since its introduction almost twenty years ago, researchers have applied it to a vast range of problem domains, including financial modelling, parallel programming and genetics. Similarly, much work has been conducted to exploit and understand the nature of its mapping scheme, triggering additional research on everything from different grammars to alternative mappers to initialization. The book first introduces GE to the novice, providing a thorough description of GE along with historical key advances. Two sections follow, each composed of chapters from international leading researchers in the field. The first section concentrates on analysis of GE and its operation, giving valuable insight into set up and deployment. The second section consists of seven chapters describing radically different applications of GE. The contributions in this volume are beneficial to both novices and experts alike, as they detail the results and researcher experiences of applying GE to large scale and difficult problems. Topics include: * Grammar design * Bias in GE * Mapping in GE * Theory of disruption in GE * Structured GE * Geometric semantic GE * GE and semantics * Multi- and Many-core heterogeneous parallel GE * Comparing methods to creating constants in GE * Financial modelling with GE * Synthesis of parallel programs on multi-cores * Design, architecture and engineering with GE * Computational creativity and GE * GE in the prediction of glucose for diabetes * GE approaches to bioinformatics and system genomics * GE with coevolutionary algorithms in cybersecurity * Evolving behaviour trees with GE for platform games * Business analytics and GE for the prediction of patient recruitment in multicentre clinical trials