Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications (Numerical Insights)

Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications (Numerical Insights)

Michael Affenzeller

Language: English

Pages: 379

ISBN: 1584886293

Format: PDF / Kindle (mobi) / ePub


Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for algorithm development.

The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimization problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems.

Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.

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In [HGL93], Homaifar and Guan for example defined a matrix element in the i-th row and the j-th column to be 1 if and only if in the tour city j is visited after city i; they also applied one- or two- point crossover on the parent matrices, which for one-point crossover means that the child tour is created by just taking the column vectors left of the crossover point from one parent, and the column vectors right of the crossover point from the other parent. Obviously, these strategies lead to

. . . Exemplary mutation of a program: The programs mutant1, mutant2, and mutant3 are possible mutants of parent. . . . Intron-augmented representation of an exemplary program in PDGP [Pol99b]. . . . . . . . . . . . . . . . . . . . . . . . . . Major preparatory steps of the basic GP process. . . . . . . The genetic programming cycle [LP02]. . . . . . . . . . . . . The GP-based problem solving process. . . . . . . . . . . . GA and GP flowcharts: The conventional genetic algorithm and genetic

which would produce a cycle into the partial tour, then the edge is not added; instead, the operator randomly selects an edge from the edges which do not produce a cycle. For example, the result of an alternating edge crossover of the parents (2 3 8 7 9 1 4 5 6) (7 5 1 6 9 2 8 4 3) could for example be (2 5 8 7 9 1 6 4 3) The first edge chosen is (1 − 2) included in the first parent’s genetic material; the second edge chosen, edge (2 − 5), is selected from the second parent, etc. The only

Then the routes are merged in descending order of their saving values if all constraints are satisfied. According to [Lap92] the time complexity of the savings heuristic is given as O(n2 log n). A lot of papers based on savings heuristics have been published. Especially Gaskell’s approach [Gas67] is appreciable in this context as it introduces a different weighting of the savings with respect to the length of the newly inserted route-part as well as the so-called parallel savings algorithm that not

(shown in the upper part) and tai75b (shown in the lower part). . . . . . . 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 11.10 11.11 11.12 11.13 11.14 11.15 Dynamic diesel engine test bench at the Institute for Design and Control of Mechatronical Systems, JKU Linz. . . . . . . Evaluation of the best model produced by GP for test strategy (1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation of the best model produced by GP for test strategy (2). . . . . . . . . . .

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