In this paper, a diversity generating mechanism is proposed for an Evolutionary Programming (EP) algorithm that determines the basic structure of Multilayer Perceptron classifiers ...
Abstract With current developments of parallel and distributed computing, evolutionary algorithms have benefited considerably from parallelization techniques. Besides improved com...
Biological populations are dynamic in both space and time, that is, the population size of a species fluctuates across their habitats over time. There are rarely any static or fix...
Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex interactions among their parameters. For last two decades, researchers have been tr...
Researchers studying Evolutionary Algorithms and their applications have always been confronted with the sample complexity problem. The relationship between population size and gl...
In multimodal function optimization, niching techniques create diversification within the population, thus encouraging heterogeneous convergence. The key to the effective diversif...
The population size in evolutionary computation is a significant parameter affecting computational effort and the ability to successfully evolve solutions. We find that population...
In an evolutionary algorithm, the population has a very important role as its size has direct implications regarding solution quality, speed, and reliability. Theoretical studies ...
Abstract. Population size for EvolutionaryAlgorithms is usually an empirical parameter. We study the population size from aspects of fitness landscapes’ ruggedness and Probably ...
With the exception of a small body of adaptive-parameter literature, evolutionary computation has traditionally favored keeping the population size constant through the course of t...