William B. Langdon – författare

Computers "that program themselves" has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main aim of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data. This issue is the main focus of this text. This book is motivated by the observation from software engineering that data abstraction (for example, via abstract data types) is essential in programs created by human programmers. It shows that abstract data types can be similarly beneficial to the automatic production of programs using GP. It shows how abstract data types (stacks, queues and lists) can be evolved using genetic programming, demonstrates how GP can evolve general programs which solve the nested brackets problem, recognizes a Dyck context free language, and implements a simple four function calculator.In these cases, an appropriate data structure is beneficial compared to simple indexed memory. The text also includes a survey of GP, with a critical review of experiments with evolving memory, and reports investigations of real world electrical network maintenance scheduling problems that demonstrate that genetic algorithms can find low cost viable solutions to such problems. This study should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, it should interest practitioners working in all these areas and those interested in automatic programming.

Computers that `program themselves' has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data. This issue is the main focus of Genetic Programming, and Data Structures: Genetic Programming + Data Structures = Automatic Programming!. This book is motivated by the observation from software engineering that data abstraction (e.g., via abstract data types) is essential in programs created by human programmers. This book shows that abstract data types can be similarly beneficial to the automatic production of programs using GP. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! shows how abstract data types (stacks, queues and lists) can be evolved using genetic programming, demonstrates how GP can evolve general programs which solve the nested brackets problem, recognises a Dyck context free language, and implements a simple four function calculator. In these cases, an appropriate data structure is beneficial compared to simple indexed memory. This book also includes a survey of GP, with a critical review of experiments with evolving memory, and reports investigations of real world electrical network maintenance scheduling problems that demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, this book will be of interest to practitioners working in all of these areas and to those interested in automatic programming.

In this volume are the proceedings of the fourth European conference on - netic Programming (EuroGP 2001) which took place at Lake Como in Italy on April, 18{20 2001. EuroGP has become r mly established as the premier - ropean event devoted to Genetic Programming. EuroGP began life in 1998 as an international workshop and was held in Paris (14{15 April, LNCS 1391). - ter that it was held in Got .. eborg, Sweden (26{27 May 1999, LNCS 1598). Its r st appearance as a conference was last year in Edinburgh in Scotland (15{ 16 April, LNCS 1802). Each year EuroGP has been co-located with a series of specialist workshops (LNCS 1468, 1596, 1803). This year was no exception and EvoWorkshops 2001 were also held at Lake Como (18-19 April, LNCS 2037). Genetic Programming (GP) refers to a branch of Evolutionary Compu- tion in which computer programs are automatically generated over a period of time using a process that mimics Darwinian evolution. The 30 papers in these proceedings more than amply demonstrate the wide and varied applicability of GP.There are papers that apply GP to robotics, artic ial retina, character recognition, nancialprediction, digitall terandelectronic circuitdesign,image processing, data fusion, and biosequencing. In addition there are many papers that address foundational and theoretical issues. Arigorousdouble-blindrefereeing systemwasappliedtothe42submitted- pers. This resulted in 17 plenary talks(40% of those submitted) and 13 research posters.

Genetic programming (GP), one of the most advanced forms of evolutionary computation, has been highly successful as a technique for getting computers to automatically solve problems without having to tell them explicitly how. Since its inceptions more than ten years ago, GP has been used to solve practical problems in a variety of application fields. Along with this ad-hoc engineering approaches interest increased in how and why GP works. This book provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.

This book constitutes the refereed proceedings of the Second European Workshop on Genetic Programming, EuroPG '99, held in Goteborg, Sweden in May 1999.The 12 revised full papers and 11 posters presented have been carefully reviewed and selected for inclusion in the book. All the relevant aspects of genetic programming are addressed ranging from traditional and foundational issues to applications in a variety of fields.

This book constitutes the refereed proceedings of the Third European Conference on Genetic Programming, EuroGP 2000, held in Scotland, UK, in April 2000. The 14 revised full papers presented together with 13 short papers were carefully reviewed and selected from a total of 39 submissions. All relevant aspects of genetic programming are addressed, ranging from theoretical and foundational issues to applications in a variety of fields such as automatic design, pattern recognition, robotic control, music and image processing, and symbolic regression.

Genetic programming (GP), one of the most advanced forms of evolutionary computation, has been highly successful as a technique for getting computers to automatically solve problems without having to tell them explicitly how. Since its inceptions more than ten years ago, GP has been used to solve practical problems in a variety of application fields. Along with this ad-hoc engineering approaches interest increased in how and why GP works. This book provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.