Chaos Genetic Algorithm Instead Genetic Algorithm
Journal: The International Arab Journal of Information Technology (Vol.12, No. 2)Publication Date: 2015-03-01
Authors : Mohammad Javidi; Roghiyeh Hosseinpourfard;
Page : 162-167
Keywords : CGA; optimization problem; chaos evolutionary algorithm.;
Abstract
Today the Genetic Algorithm (GA) is used to solve a large variety of complex nonlinear optimization problems.However, permute convergence which is one of the most important disadvantages in GA is known to increase the number of iterations for reaching a global optimum. This paper, presents a new GA based on chaotic systems to overcome this shortcoming,. We employ logistic map and tent map as two chaotic systems to generate chaotic values instead of the random values in GA processes. The diversity of the Chaos Genetic Algorithm (CGA) avoids local convergence more often than the traditional GA. Moreover, numerical results show that the proposed method decreases the number of iterations in optimization problems and significantly improves the performance of the basic GA. The idea of utilization of chaotic sequences for
optimization algorithms is motivated by biological systems such as Particle Swarm Optimization (PSO), Ant Colony algorithms (ACO) and bee colony algorithms and has the potential to improve ordinary GAs.
Other Latest Articles
- A Biometric Based Secure Session Key Agreement using Modified Elliptic Curve Cryptography
- Template Based Affix Stemmer for a Morphologically Rich Language
- Combination of Feature Selection and Optimized Fuzzy Apriori Rules: The Case of Credit Scoring
- Cloud Task Scheduling Based on Ant Colony Optimization
- A Multimodal Biometric System Based on Palmprint and Finger Knuckle Print Recognition Methods
Last modified: 2019-11-14 22:10:36