Paper Info
Title
Effects of Diversity on Optimality in GA
Abstract
Genetic Algorithms (GA) is an evolutionary inspired heuristic search algorithm. Like all heuristic search methods, the probability of locating the optimal solution is not unity. Therefore, this reduces GA's usefulness in areas that require reliable and accurate optimal solutions, such as in system modeling and control gain setting. In this paper an alteration to Genetic Algorithms (GA) is presented. This method is designed to create a specific type of diversity in order to obtain more optimal results. In particular, it is done by mutating bits that are not constant within the population. The resultant diversity and final optimality for this method is compared with standard Mutation at various probabilities. Simulation results show that this method improves search optimality for certain types of problems.
Year
DOI
Venue
2009
10.1007/978-3-642-05253-8_15
AICI
Keywords
Field
DocType
search optimality,evolutionary inspired heuristic search,genetic algorithms,optimal result,accurate optimal solution,optimal solution,final optimality,heuristic search method,certain type,resultant diversity,heuristic search,genetic algorithm,system modeling,mutation
Population,Heuristic,Mathematical optimization,Heuristic search algorithm,Computer science,Algorithm,Artificial intelligence,Systems modeling,Genetic algorithm,Machine learning
Conference
Volume
ISSN
Citations 
5855
0302-9743
0
PageRank 
References 
Authors
0.34
11
2
Name
Order
Citations
PageRank
Glen Macdonald100.34
Gu Fang216216.95