An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations

Authors:
S. H. Ling;F. H. F. Leung
Affiliations:
Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2006

Citing 0
Cited 5

Solution of nonconvex and nonsmooth economic dispatch by a new Adaptive Real Coded Genetic Algorithm

Expert Systems with Applications: An International Journal
Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect

Applied Soft Computing
Adaptive directed mutation for real-coded genetic algorithms

Applied Soft Computing
Solution to non-convex economic dispatch problem with valve point effects by incremental artificial bee colony with local search

Applied Soft Computing
Template based evolution

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a real-coded genetic algorithm (RCGA) with new genetic operations (crossover and mutation). They are called the average-bound crossover and wavelet mutation. By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning an associative-memory neural network are used to show the performance of the proposed RCGA.