This book discusses a class of algorithms which rely on analogies to natural processes - algorithms based on the principle of evolution, i.e., survival of the fittest. In these algorithms, called evolution programs, a population of individuals undergo a sequence of transformations. The individuals strive for survival: a selection scheme biased towards fitter individuals selects the next generation. After some generations, the program converges and the best individual hopefully represents the optimum solution. Hence evolution programming techniques are applicable to various hard optimization problems. The book discusses constrained optimization problems in the areas of optimal control, operations research, and engineering. The problems include optimization of functions with linear constraints, the traveling salesman problem, scheduling and partitioning problems, etc. All methods are illustrated by results obtained from various experimental systems. The book collects, in a unified and comprehensive manner, the results of evolution programming techniques previously available only in widely scattered research papers. The importance of these techniques has been growing in the last decade, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. It is aimed at researchers, practitioners, and graduate students in the areas of computer science (especially artificial intelligence), operations research, and engineering.

Genetic Algorithms + Data Structures = Evolution Programs (Symbolic Computation: Artificial Intelli)

Computers & Technology

Michalewicz, Zbigniew

Computer Science

Springer-Verlag Telos

This book provides insights drawn from the authors’ extensive experience in teaching Puzzle-based Learning. Practical advice is provided for teachers and lecturers evaluating a range of different formats for varying class sizes. Features: suggests numerous entertaining puzzles designed to motivate students to think about framing and solving unstructured problems; discusses models for student engagement, setting up puzzle clubs, hosting a puzzle competition, and warm-up activities; presents an overview of effective teaching approaches used in Puzzle-based Learning, covering a variety of class activities, assignment settings and assessment strategies; examines the issues involved in framing a problem and reviews a range of problem-solving strategies; contains tips for teachers and notes on common student pitfalls throughout the text; provides a collection of puzzle sets for use during a Puzzle-based Learning event, including puzzles that require probabilistic reasoning, and logic and geometry puzzles.

Guide to Teaching Puzzle-based Learning (Undergraduate Topics in Computer Science)

Science & Math

Falkner, Nickolas

Sooriamurthi, Raja

Meyer III, Edwin F. F.

Science for Kids

Springer

No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.

How to Solve It: Modern Heuristics

Fogel, David B.

Reference

Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address fundamental questions: What is likely to happen in the future? What is the best course of action? Adaptive Business Intelligence explores elements of data mining, predictive modeling, forecasting, optimization, and adaptability. The book explains the application of numerous prediction and optimization techniques, and shows how these concepts can be used to develop adaptive systems. Coverage includes linear regression, time-series forecasting, decision trees and tables, artificial neural networks, genetic programming, fuzzy systems, genetic algorithms, simulated annealing, tabu search, ant systems, and agent-based modeling.

Adaptive Business Intelligence

Business & Money

Michalewicz, Matthew

Schmidt, Martin

Chiriac, Constantin

Management & Leadership

In a unified and comprehensive manner, this book collects the results of evolutionary programming techniques available only in widely scattered research papers. The book includes a discussion of optimization problems in the areas of optimal control, operations research and engineering. New features are references to recent developments and a sample genetic code in C.

Genetic Algorithms + Data Structures = Evolution Programs

Michalewicz, Z.

Programming

Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.

...covers classic method of optimization, including dynamic programming, the simplex method, and gradient technique as" well as recent innovations such as simulated annealing, tabu search, and evolutionary computation.

Finance

Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address fundamental questions: What is likely to happen in the future? What is the best course of action? Adaptive Business Intelligence explores elements of data mining, predictive modeling, forecasting, optimization, and adaptability. The book explains the application of numerous prediction and optimization techniques, and shows how these concepts can be used to develop adaptive systems. Coverage includes linear regression, time-series forecasting, decision trees and tables, artificial neural networks, genetic programming, fuzzy systems, genetic algorithms, simulated annealing, tabu search, ant systems, and agent-based modeling.

See the best price to sell, buy, or rent books by Michalewicz, Zbigniew. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Michalewicz, Zbigniew

See the best price to sell, buy, or rent books by Michalewicz, Zbigniew. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Genetic Algorithms + Data Structures = Evolution Programs (Symbolic Computation: Artificial Intelli)

Guide to Teaching Puzzle-based Learning (Undergraduate Topics in Computer Science)

How to Solve It: Modern Heuristics

Adaptive Business Intelligence

Genetic Algorithms + Data Structures = Evolution Programs

Genetic Algorithms + Data Structures = Evolution Programs

How to Solve It: Modern Heuristics

How to Solve It: Modern Heuristics

Adaptive Business Intelligence

Genetic Algorithms + Data Structures = Evolution Programs