This paper describes a noise-aware dominance operator for evolutionary algorithms to solve the multiobjective optimization problems (MOPs) that contain noise in their objective fu...
Evolutionary Testing (ET) has been shown to be very successful for testing real world applications [10]. The original ET approach focusesonsearching for a high coverage of the test...
: Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Alth...
Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineeri...
Particle Swarm Optimization (PSO) technique proved its ability to deal with very complicated optimization and search problems. This paper proposes a new particle swarm variant whi...