Genetic Algorithms + Data Strcutures = Evolution Programs

The fllowing book is the second most important book in the Evolutionary Computation Course of my Master Degree on AI. The author, Zbibniew Michalewicz, is Professor at School of Computer Science in the University of Adelaide. His current research interests are in the field of evolutionary computation. He has published several books, including the monograph I’m talking about here: “Genetic Algorithms + Data Structures = Evolution Programs” 3rd, revised and extended edition, and over 200 technical papers in journals and conference proceedings.

This is the text which outline the content of the book in the back cover:

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 aim of this book is to talk about the field of evolutionary computation in simple terms, and discuss the simplicity and elegance of its methods on many interesting cases. The book may a serve as a guide to writing an evolution program, and to making this an enjoyable experience. It is self-contained and the only prerequisite is basic undergraduate mathematics. Aimed at researchers, practitioners, and graduate students, it may serve as a text for advanced courses in computer science and artificial intelligence, operations research, and engineering.

This third edition has been substantially revised and extended. Three new chapters discuss the recent paradigm of genetic programming, heuristic methods and constraint handling, and current directions of research. Additional appendices contain test functions for experiments with evolutionary techniques and discuss posiible projects for use in a project-oriented course.


Un comentario en “Genetic Algorithms + Data Strcutures = Evolution Programs

  1. One of the most important concepts which the author highlight is the fact that most evolutionary techniques, including genetic algorithms use non-binary representations.In most applications of evolutionary techniques, a population of individuals is processed, where each individual represents a solution of to problem at hand, and a selection process introduce some bias: better individuals have better chances to survive and reproduce. In the same time, a particular representaion of the individuals and the set of operators whicj alter their genetic code are often problem specific.So problem specific knowledge is incorporated by means of representation and specialized operators. There is a departure from binary-coded genetic algorithms towards more complex, problem specific systems.In general, AI problem solving strategies are categorized into “strong” and “weak” methods. A weak method makes few assumptions about the problem domain; hence it usually enjoys a wide applicability. On the other hand, it can suffer from combinatorially explosive solution costs when scaling up to larger problems. This can be avoided by making strong assumptions in the problem solving method (injecting knowledge) But a disadvantage of such strong methods are their limited applicability: very often they require significant redesign when applied even to related problems.Evolution programs fit somewhere between weak and strong methods. Some evolution programs such as genetic algorithms are quite weak without making any assumption of the problem domain. Some other programs such as evolution strategies, build to solve parameter optimization problems, are more problem specific.It is a little bit ironoc: genetic algorithms are percieved as weak methods; however, in the presence of nontrivial constraints, they change rapidly to strong methods. Wether we consider a penalty function, decoder, or a repair algorithm, these must be tailored to a specific application. On the other hand, the evolution programs percieved as much stronger, problem-dependent mnethods suddenly seem much weaker. Tis demonstrate yhe huge potential behind the evolution programmoin approach.


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