Illustrating Evolutionary Computation with MathematicaBy
- Christian Jacob
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 nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject.
Curious computing professionals, computer scientists, engineers, and biologists as well as academic and industrial researchers in AI.
Hardbound, 578 Pages
Published: January 2001
Imprint: Morgan Kaufmann
"This book provides a thorough survey of evolutionary computation techniques, including genetic algorithms, genetic programming, evolutionary programming, and evolution strategies. The author uses mathematica to illustrate the examples. If you know mathematica, you'll find this unique angle to be invaluable, but even if you don't know mathematica, if you're familiar with any programming languages, or matlab, maple, etc., you should be able to make the connections. The figures in this book have to be the most illustrative examples offered in any evolutionary computation text to date. The text is easy to read and very informative." -- Review in IEEE Computer Magazine, June issue.*5* star amazon.com review
- Part 1: Fascinating EvolutionFrom Darwin to an ArtFlowers GardenThe Fascination of EvolutionPart 2: Evolutionary ComputationEvolutionary Algorithms for OptimizationGenetic AlgorithmsEvolution StrategiesPart 3: If Darwin was a ProgrammerProgramming by Evolution; Evolutionary ProgrammingGenetic ProgrammingAdvanced Genetic Programming at WorkPart 4: Evolution of Developmental ProgramsComputer Models of Developmental ProgramsEvolutionary Inference of Lindenmayer SystemsArtificial Plant Evolution