Introduction to Genetic Algorithms
Including numerous hands-on problems and solutions, this comprehensive book is a helpful guide and a valuable source of information about Genetic Algorithm concepts for their several practical applications.
TABLE OF CONTENTS Introduction to Genetic Algorithms
Chapter 5: Classification of Genetic Algorithm
5.1 Introduction
Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. Algorithms are nothing but step-by-step procedure to find the solution to the problems. Genetic algorithms also give the step-by-step procedure to solve the problem but they are based on the genetic models. Genetic algorithms are theoretically and empirically proven to provide robust search in complex phases with the above said features. Genetic algorithms are capable of giving rose to efficient and effective search in the problem domain and hence they are now finding more wide spread application in business, scientific and engineering. These algorithms are computationally less complex but more powerful in their search for improvement. These features have enabled the researchers to form different approaches of genetic algorithm. This chapter discusses the various classifications of genetic algorithms like parallel GA, Messy GA, distributed GA and so on.
5.2 Simple Genetic Algorithm (SGA)
Many search techniques required auxiliary information in order to work properly. For e.g. Gradient techniques need derivative in order to chain the current peak and other procedures like greedy technique requires access to most tabular parameters whereas genetic algorithms do not require all these auxiliary information. GA is blind to perform an effective search for better and better structures they only require objective function values associated with the individual strings. This characteristic makes GA a more suitable method than many search schemes. GA uses probabilistic transition rules to guide their search towards regions of search space with likely improvement. Because...
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