Michael Affenzeller, Stephan Winkler, Stefan Wagner, Andreas's Genetic Algorithms and Genetic Programming: Modern Concepts PDF

By Michael Affenzeller, Stephan Winkler, Stefan Wagner, Andreas Beham

ISBN-10: 1584886293

ISBN-13: 9781584886297

Genetic Algorithms and Genetic Programming: glossy ideas and useful Applications discusses algorithmic advancements within the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to major combinatorial optimization difficulties and describes constitution id utilizing HeuristicLab as a platform for set of rules development.

The e-book specializes in either theoretical and empirical points. The theoretical sections discover the real and attribute homes of the fundamental GA in addition to major features of the chosen algorithmic extensions constructed by means of the authors. within the empirical elements of the textual content, the authors observe gasoline to 2 combinatorial optimization difficulties: the touring salesman and capacitated automobile routing difficulties. to focus on the houses of the algorithmic measures within the box of GP, they study GP-based nonlinear constitution identity utilized to time sequence and type difficulties.

Written via middle individuals of the HeuristicLab group, this publication presents a greater knowing of the fundamental workflow of fuel and GP, encouraging readers to set up new bionic, problem-independent theoretical thoughts. by way of evaluating the result of typical GA and GP implementation with numerous algorithmic extensions, it additionally indicates find out how to considerably elevate achieveable resolution quality.

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Genetic Algorithms and Genetic Programming: Modern Concepts by Michael Affenzeller, Stephan Winkler, Stefan Wagner, Andreas PDF

Genetic Algorithms and Genetic Programming: sleek suggestions and sensible purposes discusses algorithmic advancements within the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to major combinatorial optimization difficulties and describes constitution identity utilizing HeuristicLab as a platform for set of rules development.

The booklet specializes in either theoretical and empirical facets. The theoretical sections discover the $64000 and attribute houses of the elemental GA in addition to major features of the chosen algorithmic extensions built via the authors. within the empirical elements of the textual content, the authors observe fuel to 2 combinatorial optimization difficulties: the touring salesman and capacitated motor vehicle routing difficulties. to focus on the houses of the algorithmic measures within the box of GP, they research GP-based nonlinear constitution identity utilized to time sequence and type difficulties.

Written through center individuals of the HeuristicLab group, this e-book presents a greater knowing of the elemental workflow of gasoline and GP, encouraging readers to set up new bionic, problem-independent theoretical thoughts. through evaluating the result of regular GA and GP implementation with a number of algorithmic extensions, it additionally indicates how you can considerably raise conceivable answer quality.

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Extra resources for Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications

Sample text

The migration rate which determines how many individuals undergo migration at every exchange can be expressed as a percentage of the population size or as an absolute value. The majority of articles in this field suggest migration rates between 5% and 20% of the population size. However, the choice of this parameter is considered to be very problem dependent [AT99]. A recent overview of various migration techniques is given in [CP01]. Recent theory of self-adaptive selection pressure steering (see Chapters 4 and 5) plays a major role in defying the conventions of recent parallel GAtheory.

The evolvement of longer building blocks becomes more and more difficult. Decreasing the number of crossover points during the run of the GA may be a good compromise. • Uniform crossover: Given two parents, each gene in the offspring is created by copying the corresponding gene from one of the parents. The selection of the corresponding parent is undertaken via a randomly generated crossover mask: At each index, the offspring gene is taken from the first parent if there is a 1 in the mask at this index, and otherwise (if there is a 0 in the mask at this index) the gene is taken from the second parent.

The master stores the population, executes the GA operations, and distributes individuals to the slaves; the slaves compute the fitness of the individuals. As a consequence, global parallelization can be efficient only if the bottleneck in terms of runtime consumption is the evaluation of the fitness function. Globally parallel GAs are quite easy to implement, and they can be a quite efficient method of parallelization if the evaluation requires considerable computational effort compared to the effort required for the operations carried out by the master node.

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Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications by Michael Affenzeller, Stephan Winkler, Stefan Wagner, Andreas Beham


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