This book is an introduction to relevant aspects of the foraging literature for algorithmic design, and an overview of key families of optimization algorithms that stem from a foraging metaphor. The authors first offer perspectives on foraging and foraging-inspired algorithms for optimization, they then explain the techniques inspired by the behaviors of vertebrates, invertebrates, and non-neuronal organisms, and they then discuss algorithms based on formal models of foraging, how to evolve a foraging strategy, and likely future developments. No prior knowledge of natural computing is assumed. This book will be of particular interest to graduate students, academics and practitioners in computer science, informatics, data science, management science, and other application domains.

This book is an introduction to relevant aspects of the foraging literature for algorithmic design, and an overview of key families of optimization algorithms that stem from a foraging metaphor. The authors first offer perspectives on foraging and foraging-inspired algorithms for optimization, they then explain the techniques inspired by the behaviors of vertebrates, invertebrates, and non-neuronal organisms, and they then discuss algorithms based on formal models of foraging, how to evolve a foraging strategy, and likely future developments. No prior knowledge of natural computing is assumed. This book will be of particular interest to graduate students, academics and practitioners in computer science, informatics, data science, management science, and other application domains.

The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design. This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.

This book follows on from Natural Computing in Computational Finance Volumes I, II and III. As in the previous volumes of this series, the book consists of a series of chapters each of which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics. which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics. The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics. written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics.

The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design.

This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.

RecentyearshaveseentheapplicationofvariousNaturalComputing algorithms for the purposes of ?nancial modelling. In this context Natural Computing - gorithms can be broadly de?ned as computer algorithms whose design draws inspirationfromphenomena in the naturalworld. Particularfeatures of?nancial markets, including their dynamic and interconnected characteristics, bear p- allels with processes in the natural world and prima facie, this makes Natural Computingmethods'interesting'for?nancialmodellingapplications. Inaddition to the problem-solving potential of natural processes which Natural computing seeks to embody in its algorithms, we can also consider Natural Computing in terms of its potential to understand the natural processes which themselves serve as inspiration. For example, ?nancial and biological systems exhibit the phenomenon of emergence, or the activities of multiple individual agents c- bining to co-evolve their own environment, and a stream of work has emerged which applies learning mechanisms drawn from Natural Computing algorithms for the purposes of agent-based modelling in ?nance and economics. This book consists of eleven chapters each of which was selected following a rigorous,peer-reviewed,selectionprocess. Thechaptersillustratetheapplication of a range of cutting-edge natural computing and agent-based methodologies in computational ?nance and economics. While describing cutting edge appli- tions, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics,students and practitionersin the ?elds of computational ?nance and economics.

This book follows on from Natural Computing in Computational Finance Volumes I, II and III.As in the previous volumes of this series, thebook consists of a series of chapters each of

which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics.

The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are

written so that they are accessible to a wide audience. Hence, they should be of interestto academics, students and practitioners in the fields of computational finance and economics.

which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics.

The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are

written so that they are accessible to a wide audience. Hence, they should be of interestto academics, students and practitioners in the fields of computational finance and economics.

The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are

written so that they are accessible to a wide audience. Hence, they should be of interestto academics, students and practitioners in the fields of computational finance and economics.

written so that they are accessible to a wide audience. Hence, they should be of interestto academics, students and practitioners in the fields of computational finance and economics."

Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural world) for the purposes of financial modelling and optimisation. A related stream of work has also seen the application of learning mechanisms drawn from Natural Computing algorithms for the purposes of agent-based modelling in finance and economics. In this book we have collected a series of chapters which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems.

This book follows on from Natural Computing in Computational Finance (Volume 100 in Springer's Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007.

Dynamic environments abound, encompassing many real-world problems in fields as diverse as finance, engineering, biology and business. A vibrant research literature has emerged which takes inspiration from evolutionary processes to develop problem-solvers for these environments.

'Foundations in Grammatical Evolution for Dynamic Environments' is a cutting edge volume illustrating current state of the art in applying grammar-based evolutionary computation to solve real-world problems in dynamic environments. The book provides a clear introduction to dynamic environments and the types of change that can occur. This is followed by a detailed description of evolutionary computation, concentrating on the powerful Grammatical Evolution methodology. It continues by addressing fundamental issues facing all Evolutionary Algorithms in dynamic problems, such as how to adapt and generate constants, how to enhance evolvability and maintain diversity. Finally, the developed methods are illustrated with application to the real-world dynamic problem of trading on financial time-series.

The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, who are seeking to apply grammar-based evolutionary algorithms to solve problems in dynamic environments. 'Foundations in Grammatical Evolution for Dynamic Environments' is the second book dedicated to the topic of Grammatical Evolution.

Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modeling in modern computational finance. Following an introductory chapter the book is organized into three sections. The first section deals with optimization applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance.

RecentyearshaveseentheapplicationofvariousNaturalComputing algorithms for the purposes of ?nancial modelling. In this context Natural Computing - gorithms can be broadly de?ned as computer algorithms whose design draws inspirationfromphenomena in the naturalworld. Particularfeatures of?nancial markets, including their dynamic and interconnected characteristics, bear p- allels with processes in the natural world and prima facie, this makes Natural Computingmethods'interesting'for?nancialmodellingapplications. Inaddition to the problem-solving potential of natural processes which Natural computing seeks to embody in its algorithms, we can also consider Natural Computing in terms of its potential to understand the natural processes which themselves serve as inspiration. For example, ?nancial and biological systems exhibit the phenomenon of emergence, or the activities of multiple individual agents c- bining to co-evolve their own environment, and a stream of work has emerged which applies learning mechanisms drawn from Natural Computing algorithms for the purposes of agent-based modelling in ?nance and economics. This book consists of eleven chapters each of which was selected following a rigorous,peer-reviewed,selectionprocess. Thechaptersillustratetheapplication of a range of cutting-edge natural computing and agent-based methodologies in computational ?nance and economics. While describing cutting edge appli- tions, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics,students and practitionersin the ?elds of computational ?nance and economics.

Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling.

In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies - neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures.

The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain.

Dynamic environments abound, encompassing many real-world problems in fields as diverse as finance, engineering, biology and business. A vibrant research literature has emerged which takes inspiration from evolutionary processes to develop problem-solvers for these environments.

'Foundations in Grammatical Evolution for Dynamic Environments' is a cutting edge volume illustrating current state of the art in applying grammar-based evolutionary computation to solve real-world problems in dynamic environments. The book provides a clear introduction to dynamic environments and the types of change that can occur. This is followed by a detailed description of evolutionary computation, concentrating on the powerful Grammatical Evolution methodology. It continues by addressing fundamental issues facing all Evolutionary Algorithms in dynamic problems, such as how to adapt and generate constants, how to enhance evolvability and maintain diversity. Finally, the developed methods are illustrated with application to the real-world dynamic problem of trading on financial time-series.

The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, who are seeking to apply grammar-based evolutionary algorithms to solve problems in dynamic environments. 'Foundations in Grammatical Evolution for Dynamic Environments' is the second book dedicated to the topic of Grammatical Evolution.

The year 2009 celebrates the bicentenary of Darwin's birth and the 150th - niversary of the publication of his seminal work, On the Origin of Species.If this makes 2009 a special year for the research community working in biology and evolution, the ?eld of evolutionary computation (EC) also shares the same excitement. EC techniques are e?cient, nature-inspired planning and optimi- tion methods based on the principles of natural evolution and genetics. Due to their e?ciency and simple underlying principles, these methods can be used in the context of problem solving, optimization, and machine learning. A large and ever-increasing number of researchers and professionals make use of EC te- niques in various application domains. ThisvolumepresentsacarefulselectionofrelevantECapplicationscombined with a thorough examination of the techniques used in EC. The papers in the volume illustrate the current state of the art in the application of EC and can help and inspire researchers and professionals to develop e?cient EC methods for design and problem solving.

Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural world) for the purposes of financial modelling and optimisation. A related stream of work has also seen the application of learning mechanisms drawn from Natural Computing algorithms for the purposes of agent-based modelling in finance and economics. In this book we have collected a series of chapters which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems.

This book follows on from Natural Computing in Computational Finance (Volume 100 in Springer's Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007.

Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modeling in modern computational finance. Following an introductory chapter the book is organized into three sections. The first section deals with optimization applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed.

The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance.

Evolutionary computation (EC) techniques are e?cient, nature-inspired pl- ning and optimization methods based on the principles of natural evolution and genetics. Due to their e?ciency and simple underlying principles, these me- ods can be used in the context of problem solving, optimization, and machine learning. A large and continuously increasing number of researchers and prof- sionals make use of EC techniques in various application domains. This volume presents a careful selection of relevant EC examples combined with a thorough examination of the techniques used in EC. The papers in the volume illustrate the current state of the art in the application of EC and should help and - spire researchers and professionals to develop e?cient EC methods for design and problem solving. All papers in this book were presented during EvoWorkshops 2008, which consisted of a range of workshops on application-oriented aspects of EC. Since 1998, EvoWorkshops has provided a unique opportunity for EC researchers to meet and discuss applicationaspectsofECandhasservedasanimportantlink between EC research and its application in a variety of domains. During these ten years new workshops have arisen, some have disappeared, while others have matured to become conferences of their own, such as EuroGP in 2000, EvoCOP in 2004, and EvoBIO last year.

This book constitutes the refereed joint proceedings of seven workshops on evolutionary computing, EvoWorkshops 2007, held in Valencia, Spain in April 2007.

The 55 revised full papers and 24 revised short papers presented were carefully reviewed and selected from a total of 160 submissions. In accordance with the seven workshops covered, the papers are organized in topical sections on evolutionary computation in communications, networks, and connected systems, evolutionary computation in finance and economics, evolutionary computation in image analysis and signal processing, interactive evolution and humanized computational intelligence, evolutionary music and art, evolutionary algorithms in stochastic and dynamic environments, as well as evolutionary computation in transportation and logistics.

Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling.

In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures.

The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain."

This report puts forward for consideration by the House a number of initial proposals from the Leader of the House and the Clerk of the Parliaments, drawing on the recommendations of the Leader's Group. The areas covered by the proposals are: the role of the Speaker at question time and during oral statements; question time for Secretaries of State; reading out oral questions; saving time: oral questions; oral statements; questions for short debate; simpler language; and public petitions