Advances in Genetic Programming
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| Title: | Advances in Genetic Programming |
|---|---|
| Description: | There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm.There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm.Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in many of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public domain code is available, and on how to become part of the active genetic programming community via electronic mail.A major focus of the book is on improving the power of genetic programming. Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and'demes'to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality.Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition. Performance increases are demonstrated by directly evolving machine code, and implementation and design issues for genetic programming in C++ are discussed. |
| Authors: | Kenneth E. Kinnear Jr |
| Resource Type: | eBook. |
| Subjects: | Genetic programming (Computer science) |
| Categories: | COMPUTERS / Artificial Intelligence / General |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf Text: Availability: 0 |
|---|---|
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| Items | – Name: Title Label: Title Group: Ti Data: Advances in Genetic Programming – Name: Abstract Label: Description Group: Ab Data: There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm.There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm.Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in many of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public domain code is available, and on how to become part of the active genetic programming community via electronic mail.A major focus of the book is on improving the power of genetic programming. Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and'demes'to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality.Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition. Performance increases are demonstrated by directly evolving machine code, and implementation and design issues for genetic programming in C++ are discussed. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kenneth+E%2E+Kinnear+Jr%22">Kenneth E. Kinnear Jr</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+programming+%28Computer+science%29%22">Genetic programming (Computer science)</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Artificial+Intelligence+%2F+General%22">COMPUTERS / Artificial Intelligence / General</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 006.3 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Genetic programming (Computer science) Type: general Titles: – TitleFull: Advances in Genetic Programming Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kenneth E. Kinnear Jr – PersonEntity: Name: NameFull: Kenneth E. Kinnear Jr IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 1994 – D: 04 M: 02 Type: profile Y: 2014 Identifiers: – Type: isbn-print Value: 9780262111881 – Type: isbn-electronic Value: 9780262277181 Numbering: – Type: volume Value: 00001 Titles: – TitleFull: Advances in Genetic Programming Type: main |
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