Genetic Algorithm (GA)

• Published in: A Primer on Machine Learning Applications in Civil Engineering pp. 101 - 132
• Main Author: Deka, Paresh Chra
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2020; Taylor & Francis; Taylor & Francis Group
• Edition: 1
• Subjects: Civil Engineering & Construction Materials; General Engineering & Project Administration; General References; Genetic Operations; GP System; Executional Steps; Fitness Measure; Vice Versa; Hybrid GA; Genetic Operators; SVM Model; Bandit Problem; Crossover Probability; Crossover Mask; GA; Phenotypic Representation; Crossover Operation; RBF Kernel; Schema Theorem; Fitness Function; Hybrid Neuro Fuzzy System; GP; Single Point Crossover; Preparatory Steps; Parallel GA; Permutation Encoding; Mutation Probability; Real Code GA
• ISBN: 9781138323391; 113832339X
• DOI: 10.1201/9780429451423-5

J. H. Holland described how to apply the principles of natural selection to optimization problems and built the first genetic algorithms (GAs). The power of mathematics lies in the technology transfer: there exist certain models and methods, which describe many different phenomena and solve a wide variety of problems. GAs are an example of mathematical technology transfer: by simulating evolution, one can solve optimization problems from a variety of sources. The principle of GAs is simple: imitate genetics and natural selection by a computer program. An algorithm is a series of steps for solving a problem. A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. Algorithms are nothing but step-by-step procedures to find solutions to problems. A hybrid genetic algorithm has been designed by combining a variant of an already existing crossover operator with these heuristics.