UID:
almahu_9949697933102882
Format:
1 online resource (605 p.)
Edition:
1st edition
ISBN:
1-281-75495-1
,
9786611754952
,
0-08-050845-6
,
0-585-45707-7
Series Statement:
The Morgan Kaufmann Series in Artificial Intelligence
Uniform Title:
Principia evolvica.
Content:
An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in n
Note:
Description based upon print version of record.
,
Front Cover; Illustrating Evolutionary Computation with Mathematica; Copyright Page; Contents; Preface: From Darwin to an ArtFIowers Garden; Chapter 1. Introduction: The Fascination of Evolution; 1.1 Flavors of evolution; 1.2 Adaptation and selection; 1.3 Drip by drip-cumulative selection; 1.4 Simulated mimesis of butterflies; 1.5 Evolutionary creativity-biomorphs; 1.6 Bibliographical notes; Part I: Evolutionary Computation; Chapter 2. Evolutionary Algorithms for Optimization; 2.1 A simplified formal model of evolution; 2.2 Optimization through adaptive structures
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2.3 A general evolutionary algorithm scheme2.4 Bibliographical notes; Chapter 3. Genetic Algorithms; 3.1 Polyploid GA chromosomes; 3.2 Point mutation on GA chromosomes; 3.3 GA recombination; 3.4 Additional genetic operators; 3.5 Selection and GA evolution schemes; 3.6 Genetic algorithms with Evolvica; 3.7 Genetic algorithms at work; 3.8 Schemata-the key to GAs?; 3.9 Bibliographical notes; Chapter 4. Evolution Strategies; 4.1 Representation of individuals; 4.2 Mutation; 4.3 Recombination; 4.4 Selection and reproduction schemes; 4.5 Meta-evolution strategies and the island model
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4.6 Evolution strategies with Evolvica4.8 Bibliographical notes; Part II: If Darwin Had Been a Programmer; Chapter 5. Programming by Evolution; 5.1 Evolving versus programming; 5.2 Evolution and programming: a survey; 5.3 Bibliographical notes; Chapter 6. Evolutionary Programming; 6.1 Computer programs as finite state machines; 6.2 Mutation operators on FSA; Color Plates; 6.3 Automatic generation of finite state machines; 6.4 EP selection and evolution scheme; 6.5 Evolutionary programming with Evolvica; 6.6 Evolutionary programming at work; 6.7 Diversification of evolutionary programming
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6.8 Bibliographical notesChapter 7. Genetic Programming; 7.1 Symbolic expressions as genotypical program structures; 7.2 GP recombination of program structures; 7.3 GP mutation of program structures; 7.4 GP evolution scheme; 7.5 Genetic programming in action; 7.6 Bibliographical notes; Chapter 8. Advanced Genetic Programming at Work; 8.1 Breeding of computer programs: the AntTracker example; 8.2 Analysis of an evolution experiment; 8.3 Adaptive operator weights; 8.4 Advanced GP operators and functionality; 8.5 Bibliographical notes; Part III: Evolution of Developmental Programs
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Chapter 9. Computer Models of Developmental Programs9.1 Cellular automata and cellular programming; 9.2 Lindenmayer systems; 9.3 Bibliographical notes; Chapter 10. Evolutionary Inference of Lindenmayer Systems; 10.1 Encoding of IL-systems; 10.2 Evolution of fractal structures; 10.3 Bibliographical notes; Chapter 11. Artificial Plant Evolution; 11.1 The ArtFlower garden; 11.2 Breeding artificial flowers; 11.3 The ArtFIower garden in full bloom; 11.4 Extensions for more realistic plant modeling; 11.5 Evolution of plant ecosystems; 11.6 Bibliographical notes; References; Index
,
English
Additional Edition:
ISBN 1-55860-637-8
Language:
English
