2 citations · 2 across the 4 of their papers we have counts for
5 papers
Using Neural Networks and Diversifying Differential Evolution for Dynamic Optimisation
Maryam Hasani Shoreh, Renato Hermoza Aragonés, Frank Neumann
Dynamic optimisation occurs in a variety of real-world problems. To tackle these problems, evolutionary algorithms have been extensively used due to their effectiveness and minimum…
Neural Networks in Evolutionary Dynamic Constrained Optimization: Computational Cost and Benefits
Maryam Hasani-Shoreh, Renato Hermoza Aragonés, Frank Neumann
Neural networks (NN) have been recently applied together with evolutionary algorithms (EAs) to solve dynamic optimization problems. The applied NN estimates the position of the nex…
On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization
Maryam Hasani-Shoreh, Frank Neumann
Population diversity plays a key role in evolutionary algorithms that enables global exploration and avoids premature convergence. This is especially more crucial in dynamic optimi…
On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints
Maryam Hasani-Shoreh, María-Yaneli Ameca-Alducin, Wilson Blaikie +2
Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current be…
A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems
Maria-Yaneli Ameca-Alducin, Maryam Hasani-Shoreh, Wilson Blaikie +2
Dynamic constrained optimization problems (DCOPs) have gained researchers attention in recent years because a vast majority of real world problems change over time. There are studi…