Now showing items 1-10 of 79
A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems.
Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, ...
Adaptive crossover in genetic algorithms using statistics mechanism
(MIT Press, 2002)
Genetic Algorithms (GAs) emulate the natural evolution process and maintain a population of potential solutions to a given problem. Through the population, GAs implicitly maintain the statistics about the search space. ...
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
In this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimization problems. ...
A review of personal communications services.
(Nova Science Publishers., 2009)
A comparative study of immune system based genetic algorithms in dynamic environments
(ACM Press, 2006)
Diversity and memory are two major mechanisms used in biology to keep the adaptability of organisms in the ever-changing environment in nature. These mechanisms can be integrated into genetic algorithms to enhance their ...
Adaptive mutation with fitness and allele distribution correlation for genetic algorithms
(ACM Press, 2006)
In this paper, a new gene based adaptive mutation scheme is proposed for genetic algorithms (GAs), where the information on gene based fitness statistics and on gene based allele distribution statistics are correlated to ...
Genetic algorithms with elitism-based immigrants for chaning optimization problems
Addressing dynamic optimization problems has been a challenging task for the genetic algorithm community. Over the years, several approaches have been developed into genetic algorithms to enhance their performance in dynamic ...
A comparative study of adaptive mutation operators for metaheuristics.
Genetic algorithms (GAs) are a class of stochastic optimization methods inspired by the principles of natural evolution. Adaptation of strategy parameters and genetic operators has become an important and promising research ...
Particle filter with swarm move for optimization.
We propose a novel generalized algorithmic framework to utilize particle filter for optimization incorporated with the swarm move method in particle swarm optimization (PSO). In this way, the PSO update equation is treated ...