Evolutionary Algorithms

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Book Description

Evolutionary Algorithms are biologically inspired algorithms based on Darwin’s theory of evolution. They are supposed to provide suboptimal but good quality solutions to problems that cannot be solved by precise methods.

Evolutionary algorithms are successively applied to optimization problems widely in engineering, marketing, operations research, and social sciences, such as scheduling, genetics, selection materials, structural design, etc. In addition to mathematical optimization problems, evolutionary algorithms have also been used as an experimental framework in biological evolution and natural selection in the field of artificial life.

Table of contents:

Evolutionary Algorithms Preface……Page 1
Part 1……Page 13
01 Hybridization of Evolutionary Algorithms……Page 15
02 Linear Evolutionary Algorithm……Page 39
03 Genetic Algorithm Based on Schemata Theory……Page 53
04 In Vitro Fertilization Genetic Algorithm……Page 69
05 Bioluminescent Swarm Optimization Algorithm……Page 81
06 A Memetic Particle Swarm Optimization Algorithm for Network Vulnerability Analysis……Page 97
07 Quantum-Inspired Differential Evolutionary Algorithm for Permutative Scheduling Problems……Page 121
08 Quantum-Inspired Particle Swarm Optimization for Feature Selection and Parameter Optimization in Evolving Spiking Neural Networks for Classification Tasks……Page 145
09 Analytical Programming – a Novel Approach for Evolutionary Synthesis of Symbolic Structures……Page 161
10 PPCea: A Domain-Specific Language for Programmable Parameter Control in Evolutionary Algorithms……Page 189
11 Evolution Algorithms in Fuzzy Data Problems……Page 213
12 Variants of Hybrid Genetic Algorithms for Optimizing Likelihood ARMA Model Function and Many of Problems……Page 231
13 Tracing Engineering Evolution with Evolutionary Algorithms……Page 259
Part 2……Page 281
14 Evaluating the a-Dominance Operator in Multiobjective Optimization for the Probabilistic Traveling Salesman Problem with Profits……Page 283
15 Scheduling of Construction Projects with a Hybrid Evolutionary Algorithm’s Application……Page 307
16 A Memetic Algorithm for the Car Renter Salesman Problem……Page 321
17 Multi-Objective Scheduling on a Single Machine with Evolutionary Algorithm……Page 339
18 Evolutionary Algorithms in Decomposition-Based Logic Synthesis……Page 355
19 A Memory-Storable Quantum-Inspired Evolutionary Algorithm for Network Coding Resource Minimization……Page 375
20 Using Evolutionary Algorithms for Optimization of Analogue Electronic Filters……Page 393
21 Evolutionary Optimization of Microwave Filters……Page 419
22 Feature Extraction from High-Resolution Remotely Sensed Imagery using Evolutionary Computation……Page 435
23 Evolutionary Feature Subset Selection for Pattern Recognition Applications……Page 455
24 A Spot Modeling Evolutionary Algorithm for Segmenting Microarray Images……Page 471
25 Discretization of a Random Field – a Multiobjective Algorithm Approach……Page 493
26 Evolutionary Algorithms in Modelling of Biosystems……Page 507
27 Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role……Page 533
28 Evolutionary Algorithms in Crystal Structure Analysis……Page 559
29 Evolutionary Enhanced Level Set Method for Structural Topology Optimization……Page 577

Evolutionary Algorithms

Author(s): Edited by Eisuke Kita

Publisher: InTech, Year: 2011

ISBN: 9789533071718


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