ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Adaptation in genetic algorithms

Patnaik, LM and Mandavilli, S (2017) Adaptation in genetic algorithms. [Book Chapter]

Full text not available from this repository.
Official URL: https://doi.org/10.1201/9780203713402


Genetic algorithms (GAs) have emerged as effective search and optimization methods with applications in several problem domains. When the underlying search space has several locally optimal solutions apart from the globally optimal solution (i.e., the search space is multimodal), GAs emerge as worthy alternatives to traditional optimization techniques. For several years since their inception in 1975, GAs have been molded in the form proposed by Holland, characterized by constant control parameters and fixed length encodings. Recent research has led to variations in the basic GA mechanism. New selection, mutation, and crossover strategies, distributed and parallel implementations, and adaptive mechanisms to modify the control parameters have been proposed. The control parameters of a GA-crossover probability, mutation probability, and population size-critically control the performance of GAs. In the last few years, several researchers have experimented with adaptive mechanisms to dynamically vary the control parameters to improve the performance of GAs. The success that they have achieved in their pursuits makes it worthwhile to survey strategies for adapting the control parameters. In this chapter, we briefly review recent work on adaptive strategies for modifying control parameters of GAs. Next we discuss in detail our own efforts in this direction which have led to the genesis of the Adaptive Genetic Algorithm, a very effective GA variant for multimodal optimization.

Item Type: Book Chapter
Publication: Genetic Algorithms for Pattern Recognition
Publisher: CRC Press
Additional Information: The copyright for this article belongs to the Taylor & Francis.
Keywords: Gases; Optimal systems; Population statistics, Adaptive genetic algorithms; Crossover strategies; Genetic algorithm (GAs); Multi-modal optimization; Mutation probability; Optimization method; Optimization techniques; Parallel implementations, Genetic algorithms
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 05 Aug 2022 06:15
Last Modified: 05 Aug 2022 06:15
URI: https://eprints.iisc.ac.in/id/eprint/74661

Actions (login required)

View Item View Item