In this paper, a bottom-up hierarchical genetic algorithm is proposed to visualize clustered data into a planar graph. To achieve global optimization by accelerating local optimiz...
This paper describes a genetic algorithm (GA) that evolves optimized sets of coefficients for one-dimensional signal reconstruction under lossy conditions due to quantization. Beg...
In this work we provide empirical evidence that shows how a variable-length genetic algorithm (GA) can naturally evolve shorter average size populations. This reduction in chromos...
GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known...
In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world a...